Musings on Markets
A blog about markets, finance and all things money related.
In my ninth (and last) data post for 2025, I look at cash returned by businesses across the world, looking at both the magnitude and the form of that return. I start with a framework for thinking about how much cash a business can return to its owners, and then argue that, in the real world, this decision is skewed by inertia and me-tooism. I also look at a clear and discernible shift away from dividends to stock buybacks, especially in the US, and examine both good and bad reasons for this shift. After reporting on the total cash returned during the year, by public companies, in the form of dividends and buybacks, I scale the cash returned to earnings (payout ratios) and to market cap (yield) and present the cross sectional distribution of both statistics across global companies. The Cash Return Decision The decision of whether to return cash, and how much to return, should, at least in principle, be the simplest of the three corporate finance decisions, since it does not involve the estimation uncertainties that go with investment decisions and the angst of trading of tax benefits against default risk implicit in financing decisions. In practice, though, there is probably more dysfunctionality in the cash return decision, than the other two, partly driven by deeply held, and often misguided views, of what returning cash to shareholders does or does not do to a business, and partly by the psychology that returning cash to shareholders is an admission that a company's growth days are numbered. In this section, I will start with a utopian vision, where I examine how cash return decisions should play out in a business and follow up with the reality, where bad dividend/cash return decisions can drive a business over a cliff. The Utopian Version If, as I asserted in an earlier post, equity investors have a claim the cash flows left over after all needs (from taxes to debt payments to reinvestment needs) are met, dividends should represent the end effect of all of those choices. In fact, in the utopian world where dividends are residual cash flows, here is the sequence you should expect to see at businesses: In a residual dividend version of the world, companies will start with their cash flows from operations, supplement them with the debt that they think is right for them, invest that cash in good projects and the cash that is left over after all these needs have been met is available for cash return. Some of that cash will be held back in the company as a cash balance, but the balance can be returned either as dividends or in buybacks. If companies following this sequence to determine, here are the implications: The cash returned should not only vary from year to year, with more (less) cash available for return in good (bad) years), but also across firms, as firms that struggle on profitability or have large reinvestment needs might find that not only do they not have any cash to return, but that they might have to raise fresh capital from equity investors to keep going. It also follows that the investment, financing, and dividend decisions, at most firms, are interconnected, since for any given set of investments, borrowing more money will free up more cash flows to return to shareholders, and for any given financing, investing more back into the business will leave less in returnable cash flows. Seen through this structure, you can compute potential dividends simply by looking for each of the cash flow elements along the way, starting with an add back of depreciation and non-cash charges to net income, and then netting out investment needs (capital expenditures, working capital, acquisitions) as well as cash flow from debt (new debt) and to debt (principal repayments). While this measure of potential dividend has a fanciful name (free cash flow to equity), it is not only just a measure of cash left in the till at the end of the year, after all cash needs have been met, but one that is easy to compute, since every items on the list above should be in the statement of cash flows. As with almost every other aspect of corporate finance, a company's capacity to return cash, i.e., pay potential dividends will vary as it moves through the corporate life cycle, and the graph below traces the path: There are no surprises here, but it does illustrate how a business transitions from being a young company with negative free cash flows to equity (and thus dependent on equity issuances) to stay alive to one that has the capacity to start returning cash as it moves through the growth cycle before becoming a cash cow in maturity. The Dysfunctional Version In practice, though, there is no other aspect of corporate finance that is more dysfunctional than the cash return or dividend decision, partly because the latter (dividends) has acquired characteristics that get in the way of adopting a rational policy. In the early years of equity markets, in the late 1800s, companies wooed investors who were used to investing in bonds with fixed coupons, by promising them predictable dividends as an alternative to the coupons. That practice has become embedded into companies, and dividends continue to be sticky, as can be seen by the number of companies that do not change dividends each year in the graph below: While this graph is only of US companies, companies around the world have adopted variants of this sticky dividend policy, with the stickiness in absolute dividends (per share) in much of the world, and in payout ratios in Latin America. Put simply, at most companies, dividends this year will be equal to dividends last year, and if there is a change, it is more likely to be an increase than a decrease. This stickiness in dividends has created several consequences for firms. First, firms are cautious in initiating dividends, doing so only when they feel secure in their capacity to keep generate earnings. Second, since the punishment for deviating from stickiness is far worse, when you cut dividends, far more firms increase dividends than decrease them. Finally, there are companies that start paying sizable dividends, find their businesses deteriorate under them and cannot bring themselves to cut dividends. For these firms, dividends become the driving force, determining financing and investment decisions, rather than being determined by them. This is, of course, dangerous to firm health, but given a choice between the pain of announcing a dividend suspension (or cut) and being punished by the market and covering up operating problems by continuing to pay dividends, many managers choose the latter, laying th e pathway to dividend madness. Dividends versus Buybacks As for the choice of how to return that cash, i.e., whether to pay dividends or buy back stock, the basics are simple. Both actions (dividends and buybacks) have exactly the same effect on a company’s business picture, reducing the cash held by the business and the equity (book and market) in the business. It is true that the investors who receive these cash flows may face different tax consequences and that while neither action can create value, buybacks have the potential to transfer wealth from one group of shareholders (either the ones that sell back or the ones who hold on) to the other, if the buyback price is set too low or too high. It is undeniable that companies, especially in the United States, have shifted away from a policy of returning cash almost entirely in dividends until the early 1980s to one where the bulk of the cash is returned in buybacks. In the chart below, I show this shift by looking at the aggregated dividends and buybacks across S&P 500 companies from the mid-1980s to 2024: While there are a number of reasons that you can point to for this shift, including tax benefits to investors, the rise of management options and shifting tastes among institutional investors, the primary reason, in my view, is that sticky dividends have outlived their usefulness, in a business age, where fewer and fewer companies feel secure about their earning power. Buybacks, in effect, are flexible dividends, since companies, when faced with headwinds, quickly reduce or cancel buybacks, while continuing to pay dividends: In the table below, I look at the differences between dividends and buybacks: If earnings variability and unpredictability explains the shifting away from dividends, it stands to reason that this will not just be a US phenomenon, and that you will see buybacks increase across the world. In the next section, we will see if this is happening. There are so many misconceptions about buybacks that I did write a piece that looks in detail at those reasons. I do want to reemphasize one of the delusions that both buyback supporters and opponents use, i.e., that buybacks create or destroy value. Thus, buyback supporters argue that a company that is buying back its own shares at a price lower than its underlying value, is effectively taking an investment with a positive net present value, and is thus creating value. That is not true, since that action just transfers value from shareholders who sell back (at the too low a price) to the shareholders who hold on to their shares. Similarly, buyback opponents note that many companies buy back their shares, when their stock prices hit new highs, and thus risk paying too high a price, relative to value, thus destroying value. This too is false, since paying too much for shares also is a wealth transfer, this time from those who remain shareholders in the firm to those who sell back their shares. Cash Return in 2024 Given the push and pull between dividends as a residual cash flow, and the dysfunctional factors that cause companies to deviate from this end game, it is worth examining how much companies did return to their shareholders in 2024, across sectors and regions, to see which forces wins out. Cash Return in 2024 Let's start with the headline numbers. In 2024, companies across the globe returned $4.09 trillion in cash to their shareholders, with $2.56 trillion in dividends and $1.53 trillion taking the form of stock buybacks. If you are wondering how the market can withstand this much cash being withdrawn, it is worth emphasizing an obvious, but oft overlooked fact, which is that the bulk of this cash found its way back into the market, albeit into other companies. In fact, a healthy market is built on cash being returned by some businesses (older, lower growth) and being plowed back into growth businesses that need that capital. That lead in should be considered when you look at cash returned by companies, broken down by sector, in the table below, with the numbers reported both in US dollars and scaled to the earnings at these companies: To make the assessment, I first classified firms into money making and money losing, and aggregated the dividends and buybacks for each group, within each sector. Not surprisingly, the bulk of the cash bering returned is from money making firms, but the percentages of firms that are money making does vary widely across sectors. Utilities and financials have the highest percentage of money makers on the list, and financial service firms were the largest dividend payers, paying $620.3 billion in dividends in 2024, followed by energy ($346.2 billion) and industrial ($305.3 billion). Scaled to net income, dividend payout ratios were highest in the energy sector and technology companies had the lowest payout ratios. Technology companies, with $280.4 billion, led the sectors in buybacks, and almost 58% of the cash returned at money making companies in the sector took that form. Breaking down global companies by region gives us a measure of variation on cash return across the world, both in magnitude and in the type of cash return: It should come as no surprise that the United States accounted for a large segment (more than $1.5 trillion) of cash returned by all companies, driven partly by a mature economy and partly by a more activist investor base, and that a preponderance of this cash (almost 60%) takes the form of buybacks. Indian companies return the lowest percentage (31.1%) of their earnings as cash to shareholders, with the benign explanation being that they are reinvesting for growth and the not-so-benign reason being poor corporate governance. After all, in publicly traded companies, managers have the discretion to decide how much cash to return to shareholders, and in the absence of shareholder pressure, they, not surprisingly, hold on to cash, even if they do not have no need for it. It is also interesting that buybacks seems to be making inroads in other paths of the world, with even Chinese companies joining the party. FCFE and Cash Return While it is conventional practice to scale dividends to net income, to arrive at payout ratios, we did note, in the earlier section, that you can compute potential dividends from financial statements, Here again, I will start with the headline numbers again. In 2024, companies around the world collectively generated $1.66 trillion in free cash flows to equity: As you can see in the figure, companies started with net income of $6,324 billion, reinvested $4,582 billion in capital expenditures and debt repayments exceeded debt issuances by $90 billion to arrive at the free cash flow to equity of $1.66 trillion. That said, companies managed to pay out $2,555 billion in dividends and bought back $1,525 billion in stock, a total cash return of almost $4.1 trillion. As the aggregate numbers indicate, there are many companies with cash return that does not sync with potential dividends or earnings. In the picture below, we highlight four groups of companies, with the first two focused on dividends, relative to earnings, and the other two structured around cash returned relative to free cash flows to equity, where we look at mismatches. Let's start with the net income/dividend match up. Across every region of the world, 17.5% of money losing companies continue to pay dividends, just as 31% of money-making companies choose not to pay dividends. Using the free cash flows to equity to divide companies, 38% of companies with positive FCFE choose not to return any cash to their shareholder while 48% of firms with negative FCFE continue to pay dividends. While all of these firms claim to have good reasons for their choices, and I have listed some of them, dividend dysfunction is alive and well in the data. I argued earlier in this post that cash return policy varies as companies go through the life cycle, and to see if that holds, we broke down global companies into deciles, based upon corporate age, from youngest to oldest, and looked at the prevalence of dividends and buybacks in each group: As you can see, a far higher percent of the youngest companies are money-losing and have negative FCFE, and it is thus not surprising that they have the lowest percentage of firms that pay dividends or buy back stock. As companies age, the likelihood of positive earnings and cash flows increases, as does the likelihood of dividend payments and stock buybacks. Conclusion While dividends are often described as residual cash flows, they have evolved over time to take on a more weighty meaning, and many companies have adopted dividend policies that are at odds with their capacity to return cash. There are two forces that feed this dividend dysfunction. The first is inertia, where once a company initiates a dividend policy, it is reluctant to back away from it, even though circumstances change. The second is me-tooism, where companies adopt cash return policies to match their peer groups, paying dividends because other companies are also paying dividends, or buying back stock for the same reasons. These factors explain so much of what we see in companies and markets, but they are particularly effective in explaining the current cash return policies of companies. YouTube Data Updates for 2025 Data Update 1 for 2025: The Draw (and Danger) of Data! Data Update 2 for 2025: The Party continued for US Equities Data Update 3 for 2025: The times they are a'changin'! Data Update 4 for 2025: Interest Rates, Inflation and Central Banks! Data Update 5 for 2025: It's a small world, after all! Data Update 6 for 2025: From Macro to Micro - The Hurdle Rate Question! Data Update 7 for 2025: The End Game in Business! Data Update 8 for 2025: Debt, Taxes and Default - An Unholy Trifecta! Data Update 9 for 2025: Dividend Policy - Inertia and Me-tooism Rule! Data Links Dividend fundamentals, by industry (US, Global, Emerging Markets, Europe, Japan, India, China) Cash return and FCFE, by industry (US, Global, Emerging Markets, Europe, Japan, India, China)
There is a reason that every religion inveighs against borrowing money, driven by a history of people and businesses, borrowing too much and then paying the price, but a special vitriol is reserved for the lenders, not the borrowers, for encouraging this behavior. At the same time, in much of the word, governments have encouraged the use of debt, by providing tax benefits to businesses (and individuals) who borrow money. In this post, I look at the use of debt by businesses, around the globe, chronicling both the magnitude of borrowing, and the details of debt (in terms of maturity, fixed vs floating, straight vs convertible). The tension between borrowing too little, and leaving tax benefits on the table, and borrowing too much, and exposing yourself to default risk, is felt at every business, but the choice of how much to borrow is often driven by a range of other considerations, some of which are illusory, and some reflecting the frictions of the market in which a business operates. The Debt Trade off As a prelude to examining the debt and equity tradeoff, it is best to first nail down what distinguishes the two sources of capital. There are many who trust accountants to do this for them, using whatever is listed as debt on the balance sheet as debt, but that can be a mistake, since accounting has been guilty of mis-categorizing and missing key parts of debt. To me, the key distinction between debt and equity lies in the nature of the claims that its holders have on cash flows from the business. Debt entitles its holders to contractual claims on cash flows, with interest and principal payments being the most common forms, whereas equity gives its holders a claim on whatever is left over (residual claims). The latter (equity investors) take the lead in how the business is run, by getting a say in choosing who manages the business and how it is run, while lenders act, for the most part, as a restraining influence. Using this distinction, all interest-bearing debt, short term and long term, clears meets the criteria for debt, but for almost a century, leases, which also clearly meet the criteria (contractually set, limited role in management) of debt, were left off the books by accountants. It was only in 2019 that the accounting rule-writers (IFRS and GAAP) finally did the right thing, albeit with a myriad of rules and exceptions. Every business, small or large, private or public and anywhere in the world, faces a question of whether to borrow money, and if so, how much, and in many businesses, that choice is driven by illusory benefits and costs. Under the illusory benefits of debt, I would include the following: Borrowing increases the return on equity, and is thus good: Having spent much of the last few decades in New York, I have had my share of interactions with real estate developers and private equity investors, who are active and heavy users of debt in funding their deals. One reason that I have heard from some of them is that using debt allows them to earn higher returns on equity, and that it is therefore a better funding source than equity. The first part of the statement, i.e., that borrowing money increases the expected return on equity in an investment, is true, for the most part, since you have to contribute less equity to get the deal done, and the net income you generate, even after interest payments, will be a higher percentage of the equity invested. It is the second part of the statement that I would take issue with, since the higher return on equity, that comes with more debt, will be accompanied by a higher cost of equity, because of the use of that debt. In short, I would be very skeptical of any analysis that claims to turn a neutral or bad project, funded entirely with equity, into a good one, with the use of debt, especially when tax benefits are kept out of the analysis. The cost of debt is lower than the cost of equity: If you review my sixth data update on hurdle rates, and go through my cost of capital calculation, there is one inescapable conclusion. At every level of debt, the cost of equity is generally much higher than the cost of debt for a simple reason. As the last claimants in line, equity investors have to demand a higher expected return than lenders to break even. That leads some to conclude, wrongly, that debt is cheaper than equity and more debt will lower the cost of capital. (I will explain why later in the post.) Under the illusory costs of debt, here are some that come to mind: Debt will reduce profits (net income): On an absolute basis, a business will become less profitable, if profits are defined as net income, if it borrows more money. That additional debt will give rise to interest expenses and lower net income. The problem with using this rationale for not borrowing money is that it misses the other side of debt usage, where using more debt reduces the equity that you will have to invest. Debt will lower bond ratings: For companies that have bond ratings, many decisions that relate to use of debt will take into account what that added debt will do to the company’s rating. When companies borrow more money, it may seem obvious that default risk has increased and that ratings should drop, because that debt comes with contractual commitments. However, remember that the added debt is going into investments (projects, joint ventures, acquisitions), and these investments will generate earnings and cash flows. When the debt is within reasonable bounds (scaling up with the company), a company can borrow money, and not lower its ratings. Even if bond ratings drop, a business may be worth more, at that lower rating, if the tax benefits from the debt offset the higher default risk. Equity is cheaper than debt: There are businesspeople (including some CFOs) who argue that debt is cheaper than equity, basing that conclusion on a comparison of the explicit costs associated with each – interest payments on debt and dividends on equity. By that measure, equity is free at companies that pay no dividends, an absurd conclusion, since investors in equity anticipate and build in an expectation of price appreciation. Equity has a cost, with the expected price appreciation being implicit, but it is more expensive than debt. The picture below captures these illusory benefits and costs: If the above listed are illusory reasons for borrowing or not borrowing, what are the real reasons for companies borrowing money or not borrowing? The two primary benefits of borrowing are listed below: Tax Benefits of Debt: The interest expenses that you have on debt are tax deductible in much of the world, and that allows companies that borrow money to effectively lower their cost of borrowing: After-tax cost of debt = Interest rate on debt (1 – tax rate) In dollar terms, the effect is similar; a firm with a 25% tax rate and $100 million in interest expenses will get a tax benefit of $25 million, from that payment. Debt as a disciplinary mechanism: In some businesses, especially mature ones with lots of earnings and cash flows, managers can become sloppy in capital allocation and investment decisions, since their mistakes can be covered up by the substantial earnings. Forcing these companies to borrow money, can make managers more disciplined in project choices, since poor projects can trigger default (and pain for managers). These have to be weighted off against two key costs: Expected bankruptcy costs: As companies borrow money, the probability that they will be unable to make their contractual payments on debt will always increase, albeit at very different rtes across companies, and across time, and the expected bankruptcy cost is the product of this probability of default and the cost of bankruptcy, including both direct costs (legal and deadweight) and indirect costs (arising from the perception that the business is in trouble). Agency costs: Equity investors and lenders both provide capital to the business, but the nature of their claims (contractual and fixed for debt versus residual for equity) creates very different incentives for the two groups. In short, what equity investors do in their best interests (taking risky projects, borrow more money or pay dividends) may make lenders worse off. As a consequence, when lending money, lenders write in covenants and restrictions on the borrowing businesses, and those constraints will cause costs (ranging from legal and monitoring costs to investments left untaken). The real trade off on debt is summarized in the picture below: While the choices that businesses make on debt and equity should be structured around expected tax benefits (debt’s biggest plus) and expected bankruptcy costs (debt’s biggest minus), businesses around the world are affected by frictions, some imposed by the markets that they operate in, and some self-imposed. The biggest frictional reasons for borrowing are listed below: Bankruptcy protections (from courts and governments): If governments or courts step in to protect borrowers, the former with bailouts, and the latter with judgments that consistently favor borrowers, they are nullifying the effect of expected bankruptcy costs in restraining companies from borrowing too much. Consequently, companies in these environments will borrow much more than they should. Subsidized Debt: If lenders or governments lend money to firms at below-market reasons for reasons of virtue (green bonds and lending) or for political/economic reasons (governments lending to companies that choose to keep their manufacturing within the domestic economy), it is likely that companies will borrow much more than they would have without these debt subsidies. Corporate control: There are companies that choose to borrow money, even though debt may not be the right choice for them, because the inside investors in these companies (family groups, founders) do not want to raise fresh equity from the market, concerned that the new shares issued will reduce their power to control the firm. The biggest frictional reasons for holding back on borrowing include: Debt covenants: To the extent that debt comes with restrictions, a market where lender restrictions are more onerous in terms of the limits that they put on what borrowers can or cannot do will lead to a subset of companies that value flexibility borrowing less. Overpriced equity: To the extent that markets may become over exuberant about a company's prospects, and price its equity too highly, they also create incentives for these firms to overuse equity (and underutilize debt). Regulatory constraints: There are some businesses where governments and regulators may restrict how much companies operating in them can borrow, with some of these restrictions reflecting concerns about systemic costs from over leverage and others coming from non-economic sources (religious, political). The debt equity trade off, in frictional terms, is in the picture below: As you look through these trade offs, real or frictional, you are probably wondering how you would put them into practice, with a real company, when you are asked to estimate how much it should be borrow, with more specificity. That is where the cost of capital, the Swiss Army Knife of finance that I wrote about in my sixth data update update, comes into play as a debt optimizing tool. Since the cost of capital is the discount rate that you use to discount cash flows back to get to a value, a lower cost of capital, other things remaining equal, should yield a higher value, and minimizing the cost of capital should maximize firm. With this in place, the “optimal” debt mix of a business is the one that leads to the lowest cost of capital: You will notice that as you borrow more money, replacing more expensive equity with cheaper debt, you are also increasing the costs of debt and equity, leading to a trade off that can sometimes lower the cost of capital and sometimes increase it. This process of optimizing the debt ratio to minimize the cost of capital is straight forward, and if you are interested, this spreadsheet will help you do this for any company. Measuring the Debt Burden With that tradeoff in place, we are ready to examine how it played out in 2024, by looking at how much companies around the world borrowed to fund their operations. We can start with dollar value debt, with two broad measures – gross debt, representing all interest-bearing debt and lease debt, and net debt, which nets cash and marketable securities from gross debt. In 2024, here are the gross and net debt values for global companies, broken down by sector and sub-region: The problem with dollar debt is that absolute values can be difficult to compare across sectors and markets with very different values, I will look at scaled versions of debt, first to total capital (debt plus equity) and then then to rough measures of cash flows (EBITDA) and earnings (EBIT). The picture below lists the scaled versions of debt: Debt to Capital: The first measure of debt is as a proportion of total capital (debt plus equity), and it is this version that you use to compute the cost of capital. The ratio, though, can be very different when you use book values for debt and equity then when market values are used. The table below computes debt to capital ratios, in book and market terms, by sector and sub-region: I would begin by separating the financial sector from the rest of the market, since debt to banks is raw material, not a source of capital. Breaking down the remaining sectors, real estate and utilities are the heaviest users of debt, and technology and health care the lightest. Across regions, and looking just at non-financial firms, the US has the highest debt ratio, in book value terms, but among the lowest in market value terms. Note that the divergence between book and market debt ratios in the last two columns varies widely across sectors and regions. Debt to EBITDA: Since debt payments are contractually set, looking at how much debt is due relative to measure of operating cash flow making sense, and that ratio of debt to EBITDA provides a measure of that capacity, with higher (lower) numbers indicating more (less) financial strain from debt. Interest coverage ratio: Interest expenses on debt are a portion of the contractual debt payments, but they represent the portion that is due on a periodic basis, and to measure that capacity, I look at how much a business generates as earnings before interest and taxes (operating income), relative to interest expenses. In the table below, I look at debt to EBITDA and interest coverage ratios, by region and sector: The results in this table largely reaffirm our findings with the debt to capital ratio. Reda estate and utilities continue to look highly levered, and technology carries the least debt burden. Across regions, the debt burden in the US, stated as a multiple of EBITDA or looking at interest coverage ratios, puts it at or below the global averages, whereas China has the highest debt burden, relative to EBITDA. The Drivers and Consequences of Debt As you look at differences in the use of debt across regions and sectors, it is worth examining how much of these differences can be explained by the core fundamentals that drive the debt choice – the tax benefits of debt and the bankruptcy cost. The tax benefit of debt is the easier half of this equation, since it is directly affected by the marginal tax rate, with a higher marginal tax rate creating a greater tax benefit for debt, and a greater incentive to borrow more. Drawing on a database maintained by PWC that lists marginal tax rates by country, I create a heat map: The country with the biggest changes in corporate tax policy in the world, for much of the last decade, has been the United States, where the federal corporate tax rate, which at 35%, was one of the highest in the world prior to 2017, saw a drop to 21% in 2017, as part of the first Trump tax reform. With state and local taxes added on, the US, at the start of 2025, had a marginal corporate tax rate of 25%, almost perfectly in line with a global norm. The 2017 tax code, though, will sunset at the end of 2025, and corporate tax rates will revert to their old levels, but the Trump presidential win has not only increased the odds that the 2017 tax law changes will be extended for another decade, but opened up the possibility that corporate tax rates may decline further, at least for a subset of companies. An interesting question, largely unanswered or answered incompletely, is whether the US tax code change in 2017 changed how much US companies borrowed, since the lowering of tax rates should have lowered the tax benefits of borrowing. In the table below, I look at dollar debt due at US companies every year from 2015 to 2024, and the debt to EBITDA multiples each year: As you can see, the tax reform act has had only a marginal effect on US corporate leverage, albeit in the right direction. While the dollar debt at US companies has continued to rise, even after marginal tax rates in the US declines, the scaled version of debt (debt to capital ratio and debt to EBITDA have both decreased). The most commonly used measure of default risk is corporate bond ratings, since ratings agencies respond (belatedly) to concerns about default risk by downgrading companies. The graph below, drawing on data from S&P< looks at the distribution of bond ratings, from S&P, of rated companies, across the globe, and in the table below, we look at the breakdown by sector: The ratings are intended to measure the likelihood of default, and it is instructive to look at actual default rates over time. In the graph below, we look at default rates in 2024, in a historical context: S&P As you can see in the graph, default rates are low in most periods, but, not surprisingly, spike during recessions and crises. With only 145 corporate defaults, 2024 was a relatively quiet year, since that number was slightly lower than the 153 defaults in 2023, and the default rate dropped slightly (from 3.6% to3.5%) during the year. The default spread is a price of risk in the bond market, and if you recall, I estimated the price of risk in equity markets, with an implied equity risk premium, in my second data update. To the extent that the price of risk in both the equity and debt markets are driven by the endless tussle between greed and fear, you would expect them to move together much of the time, and as you can see in the graph below, I look at the implied equity risk premium and the default spread on a Baa rated bond: Damodaran.com In 2024, the default spread for a Baa rated dropped from 1.61% to 1.42%, paralleling a similar drop in the implied equity risk premium from 4.60% to 4.33%. Debt Design There was a time when businesses did not have much choice, when it came to borrowing, and had to take whatever limited choices that banks offered. In the United States, corporate bond markets opened up choices for US companies, and in the last three decades, the rest of the world has started to get access to domestic bond markets. Since corporate bonds lend themselves better than bank loans to customization, it should come as no surprise now that many companies in the world have literally dozens of choices, in terms of maturity, coupon (fixed or floating), equity kickers (conversion options) and variants on what index the coupon payment is tied to. While these choices can be overwhelming for some companies, who then trust bankers to tell them what to do, the truth is that the first principles of debt design are simple. The best debt for a business is one that matches the assets it is being used to fund, with long term assets funded with long term debt, euro assets financed with euro debt, and with coupon payments tied to variables that also affect cash flows. There is data on debt design, though not all companies are as forthcoming about how their debt is structured. In the table below, I look at broad breakdowns – conventional and lease debt, long term and short debt, by sector and sub-region again: The US leads the world in the use of lease debt and in corporate bonds, with higher percentages of total debt coming from those sources. However, floating rate debt is more widely used in emerging markets, where lenders, having been burned by high and volatile inflation, are more likely to tie lending rates to current conditions. While making assessments of debt mismatch requires more company-level analysis, I would not be surprised if inertia (sticking with the same type of debt that you have always uses) and outsourcing (where companies let bankers pick) has left many companies with debt that does not match their assets. These companies then have to go to derivatives markets and hedge that mismatch with futures and options, creating more costs for themselves, but fees and benefits again for those who sell these hedging products. Bottom Line When interest rates in the United States and Europe rose strongly in 2022, from decade-long lows, there were two big questions about debt that loomed. The first was whether companies would pull back from borrowing, with the higher rates, leading to a drop in aggregate debt. The other was whether there would be a surge in default rates, as companies struggled to generate enough income to cover their higher interest expenses. While it is still early, the data in 2023 and 2024 provide tentative answers to these questions, with the findings that there has not been a noticeable decrease in debt levels, at least in the aggregate, and that while the number of defaults has increased, default rates remain below the highs that you see during recessions and crises. The key test for companies will remain the economy, and the question of whether firms have over borrowed will be a global economic slowdown or recession. YouTube Video Data Updates for 2025 Data Update 1 for 2025: The Draw (and Danger) of Data! Data Update 2 for 2025: The Party continued for US Equities Data Update 3 for 2025: The times they are a'changin'! Data Update 4 for 2025: Interest Rates, Inflation and Central Banks! Data Update 5 for 2025: It's a small world, after all! Data Update 6 for 2025: From Macro to Micro - The Hurdle Rate Question! Data Update 7 for 2025: The End Game in Business! Data Update 8 for 2025: Debt, Taxes and Default - An Unholy Trifecta! Data Links Debt fundamentals, by industry (US, Global, Emerging Markets, Europe, Japan, India & China) Debt details, by industry (US, Global, Emerging Markets, Europe, Japan, India & China)
While I was working on my last two data updates for 2025, I got sidetracked, as I am wont to do, by two events. The first was the response that I received to my last data update, where I looked at the profitability of businesses, and specifically at how a comparison of accounting returns on equity (capital) to costs of equity (capital) can yield a measure of excess returns. The second was a comment that I made on a LinkedIn post that had built on my implied equity premium approach to the Indian market but had run into a roadblock because of an assumption that, in an efficient market, the return on equity would equate to the cost of equity. I pointed to the flaw in the logic, but the comments thereafter suggested such deep confusion about what returns on equity or capital measure, and what comprises an efficient market, that I think it does make sense to go back to basics and see if some of the confusion can be cleared up. The Lead In: Business Formation To keep this example as stripped of complexity as I can, at least to begin, I will start with two entrepreneurs who invest $60 million apiece to start new businesses, albeit with very different economics: The first entrepreneur starts business A, with a $60 million investment up front, and that business is expected to generate $15 million in net income every year in perpetuity. The second entrepreneur starts business B, again with a $60 million investment up front, and that investment is expected to generate $3 million every year in perpetuity. With these characteristics, the accounting balance sheets for these companies will be identical right after they start up, and the book value of equity will be $60 million in each company. The return on equity is an entirely accounting concept, and it can be computed by dividing the net income of each of the two businesses by the book value of equity: Return on equity for Business A = Net income for Business A / Book Value of Equity for Business A = 15/60 = 25% Return on equity for Business B = Net income for Business B / Book Value of Equity for Business B = 3/60 = 5% Assume that both these businesses have the same underlying business risk that translates into a cost of equity of 10%, giving the two businesses the following excess returns: Excess Return for Business A = Return on equity for Business A – Cost of equity for Business A = 25% -10% = 15% Excess Return for Business B = Return on equity for Business B – Cost of equity for Business B = 5% -10% = -5% In the language of my last post, the first business is a good one, because it creates value by earning more than your money would have earned elsewhere on an investment of equivalent risk, and the second is a bad one, because it does not. The return on equity may be an equation that comes from accounting statements, but in keeping with my argument that every number needs a narrative, each of these numbers has a narrative, often left implicit, that should be made explicit. On business A, the story has to be one of strong barriers to entry that allow it to sustain its excess returns in perpetuity, and those could include anything from a superlative brand name to patent protection to exclusive access to a natural resource. In the absence of these competitive advantages, these excess returns would have faded very quickly over time. On business B, you have a challenge, since it does seem irrational that an entrepreneur would enter a bad business, and while that irrationality cannot be ruled out (perhaps the entrepreneur thinks that earning any profit makes for a good business), the reality is that outside events can wreak havoc on the bet paid plans of businesses. For instance, it is possible that the entrepreneur’s initial expectations were that he or she would earn much more than 5%, but a competitor launching a much better product or a regulatory change could have changed those expectations. In sum, the return on equity and its more expansive variant, the return on invested capital, measure what a company is making on the capital it has invested in business, and is a measure of business quality. The Market Launch Assume now that the owners of both businesses (A and B) list their businesses in the market, disclosing what they expect to generate as net income in perpetuity. Investors in equity markets will now get a chance to price the two companies, and if markets are efficient, they will arrive at the following: Thus, a discerning (efficient) market would value business A, with $15 million in net income in perpetuity at $150 million, while valuing business B, with $3 million in net income in perpetuity, at $30 million. If you are wondering why you would discount net income, rather than cash flow, the unique features of these investments (constant net income, no growth and forever lives) makes net income equal to cash flow. Even with this very simplistic example, there are useful implications. The first is that if markets are efficient, the price to book ratios will reflect the quality of these companies. In this example, for instance, business A, with a market value of equity of $150 million and a book value of equity of $60 million, will trade at 2.50 times book value, whereas company B with a market value of equity of $30 million and a book value of equity of $60 million will trade at half of book value. Both companies would be fairly valued, though the first trades at well above book value and the second at well below, thus explaining why a lazy variant of value investing, built almost entirely on buying stocks that trade at low price to book ratio,, will lead you to holding bad businesses, not undervalued ones. As I noted at the start of this post, it was motivated by trying to clear up a fundamental misunderstanding of what return on equity measures. In fact, the working definition that some commenters used for return on equity was obtained by dividing the net income by the market value of equity. That is not return on equity, but an earnings to price ratio, i.e., the earnings yield, and in these examples, with no growth and perpetual (constant) net income, that earnings yield will be equal to the cost of equity in an efficient market. Extending the Discussion One of the advantages of this very simple illustration is that it now can be used as a launching pad for casting light on some of the most interesting questions in investing: Good companies versus Good Investments: I have written about the contrast between a good company and a good investment, and this example provides an easy way to illustrate the difference. Looking at companies A and B, there is absolutely no debating the fact that company A is better company, with sustainable moats and high returns on equity (25%), than company B, which struggles to make money (return on equity of 5%), and clearly is in a bad business. However, which of these two companies is the better investment rests entirely on how the market prices them: As you can see, the good company (A) can be a good, bad or neutral investment, depending on whether its is priced at less than, greater than or equal to its fair value ($150 million) and the same can be said about the bad company (B), with the price relative to its fair value ($30 million). At fair value, both become neutral investments, generating returns to shareholders that match their cost of equity. The Weakest Link in Excess Returns: The excess return is computed as the difference between return on equity and the cost of equity, and while it is true that different risk and return models and differences in risk parameters (relative risk measures and equity risk premiums) can cause variations in cost of equity calculations, the return on equity is the weaker link in this comparison. To understand some of the ways the return on equity can be skewed, consider the following variants on the simple example in this case: Accounting inconsistencies: As an entirely accounting number, the return on equity is exposed to accounting inconsistencies and miscategorization. To illustrate with our simple example, assume that half the money invested in business A is in R&D, which accountants expense, instead of capitalizing. That business will report a loss of $15 million (with the R&D expense of $30 million more than wiping out the profit of $15 million) in the first year on book capital of $30 million (the portion of the capital invested that is not R&D), but in the years following, it will report a return on capital of 50.00% (since net income will revert back to $15 million, and equity will stay at $30 million). Carrying this through to the real world, you should not be surprised to see technology and pharmaceutical companies, the two biggest spenders on R&D, report much higher accounting returns than they are actually earning on their investments.. Aging assets: In our example, we looked at firms an instant after the upfront investment was made, when the book value of investment measures what was paid for the assets acquired. As assets age, two tensions appear that can throw off book value, the first being inflation, which if not adjusted for, will result in the book value being understated, and accounting returns overstated. The other is accounting depreciation, which often has little to do with economic depreciation (value lost from aging), and subject to gaming. Extrapolating, projects and companies with older assets will tend to have overstated accounting returns, as inflation and depreciation lay waste to book values. In fact, with an aging company, and adding in stock buybacks, the book value of equity can become negative (and is negative for about 10% of the companies in my company data sample). Fair Value Accounting: For the last few decades, the notion of fair value accounting has been a fever dream for accounting rule writers, and those rules, albeit in patchwork form, have found their way into corporate balance sheets. In my view, fair value accounting is pointless, and I can use my simple example to illustrate why. If you marked the assets of both company A and company B to market, you would end with book values of $150 million and $30 million for the two companies and returns on equity of 10% for both firms. In short, if fair value accounting does what it is supposed to do, every firm in the market will earn a return on equity (capital) equal to the cost of equity (capital), rendering it useless as a metric for separating good and bad businesses. If fair value accounting fails at what it is supposed to do, which is the more likely scenario, you will end up with book values of equity that measure neither original capital invested nor current market value, and returns on equity and capital that become noise. Growth enters the equation: For companies A and B, in this example, we assumed that the net income was constant, i.e., there is no growth. Introducing growth into the equation changes none of the conclusions that we have drawn so far, but it makes reading both the return on equity and the earnings yield much messier. To see why, assume that company A in the example continues to have no growth, but company B expects to see compounded annual growth of 50% a year in its net income of $3 million for the next decade. We can no longer consign company B to the bad business pile as easily, and the current earnings to price ratio for that company will no longer be equal to the cost of equity, even if markets are efficient. Incorporating growth into the analysis will also mean that net income is not equal to cash flow, since some or a large portion of that net income will have to get reinvested back to deliver the growth. In fact, this is the argument that I used in my second data update to explain why comparing the earnings yield to the treasury bond rate is unlikely to yield a complete assessment of whether stocks are under or over valued, since it ignores growth and reinvestment entirely. Exiting bad businesses: This example also helps to bring home why it is so difficult for companies in bad businesses to fix their "badness" or exit their businesses. In the case of company B, for instance, telling the manager to find projects that earn more than 10% is advice that can be freely dished out, but how exactly do you invent good projects in a business that has turned bad? While exiting the business seems to be a better choice, that presupposes that you will get your capital ($60 million) back when you do, but in the real world, potential buyers will discount that value. In fact, if you divest or sell the bad business for less than $30 million, you are actually worse off than staying in the business and continuing to generate $3 million a year in perpetuity, which has a $30 million value. In the real world, most companies in bad businesses hire new CEOs, restructure their businesses and enter new businesses in a desperate attempt to become good businesses, and enrich consultants and bankers, but not their own shareholders, along the way. Conclusion Many of the comments on my seventh data update, and on my explanation about why ROE and cost of equity don’t have to be equal in an efficient market, came from people with degrees and certifications in finance, and quite a few of the commenters had “finance professional” listed in their profile. Rather than take issue with them, I would argue that this misunderstanding of basics is a damning indictment of how these concepts and topics are taught in the classroom, and since I may very well be one of the culprits, one reason that I wrote this post is to remind myself that I have to revisit the basics, before making ambitious leaps into corporate financial analysis and valuation. For those of you who are not finance professionals, but rely on them for advice, I hope this is a cautionary note on taking these professionals (consultants, appraisers, bankers) at their word. Some of them throw buzzwords and metrics around, with little understanding of what they mean and how they are related, and it is caveat emptor. YouTube Video
I am in the third week of the corporate finance class that I teach at NYU Stern, and my students have been lulled into a false sense of complacency about what's coming, since I have not used a single metric or number in my class yet. In fact, we have spent almost four sessions (that is 15% of the overall class) talking about the end game in business. In an age when ESG, sustainability and stakeholder wealth maximization have all tried to elbow their way to the front of the line, all laying claim to being what business should be about, I have burnished my "moral troglodyte" standing by sticking with my belief that the end game in business is to maximize value, with earnings and cash flows driving that value, and that businesses that are profitable and value creating are in a much better position to do good, if they choose to try. In this post, I will focus on how companies around the world, and in different sectors, performed on their end game of delivering profits, by first focusing on profitability differences across businesses, then converting profitability into returns, and comparing these returns to the hurdle rates that I talked about in my last data update post. Profitability - Absolute and Relative While we may all agree with the proverbial bottom line being profits, there seems to be no consensus on how best to measure profitability, either from an accounting or an economic perspective. In this section, I will begin with a simplistic breakdown of the income statement, the financial statement that is supposed to tell us how much a business generated in profits in during a period, and use it as an (imperfect) tool to understand the business economics. While accountants remain focused on balance sheets, with a fixation of bringing intangibles on to the balance and marking everything up to the market, much of the information that we need to assess the value of a business comes from income and cash flow statements. I am not an accountant, but I do rely on accounting statements for the raw data that I use in corporate finance and valuation. I have tried my hand at financial statement analysis, as practiced by accountants, and discovered that for the most part, the analysis creates more confusions than clarity, as a multiplicity of ratios pull you in different directions. It is for that reason that I created my own version of an accounting class, that you can find on my webpage. During the course of the class, I assess the income statement, in its most general form, by looking at the multiple measures of earnings at different phases of the statement: Which of these represents the bottom line for businesses? If you are a shareholder in a company, i.e., an equity investor, the measure that best reflects the profits the company made on the equity you invested in them is the earnings per share. That said, there is information in the measures of earnings as you climb the income statement, and there are reasons why as you move up the income statement, the growth rates you observe may be different: To get from net income to earnings per share, you bring in share count, and actions taken by companies that alter that share count will have effects. Thus, a company that issues new shares to fund its growth may see net income growth, but its earnings per share growth will lag, as the share count increases. Conversely, a company that buys back shares will see share count drop, and earnings per share growth will outpace net income growth. To get from operating income to net income, you have multiple variables to control for. The first is taxes, and incorporating its effect will generally lead to lower net income, and the tax rate that you pay to get from pretax profit to net income is the effective tax rate. To the extent that you have cash on your balance, you will generate interest income which adds on to net income, but interest expenses on debt will reduce income, with the net effect being positive for companies with large cash balance, relative to the debt that they owe, and negative for firms with large net debt outstanding. There is also the twist of small (minority) holdings in other companies and the income you generate from those holdings that affect net income. To get from gross income to operating income, you have to bring in operating expenses that are not directly tied to sales. Thus, if you have substantial general and administrative costs or incur large selling and advertising costs or if you spend money on R&D (which accountants mistakenly still treat as operating expenses), your operating income will be lower than your gross income. Finally, to get from revenues to gross income, you net out the expenses incurred on producing the goods/services that you sell, with these expenses often bundled into a "cost of goods sold" categorization. While depreciation of capital investments made is usually separated out from costs of goods sold, and shown as an operating cost, there are some companies, where it is bundled into costs of goods sold. In many cases, the only statement where you will see depreciation and amortization as a line item is the statement of cash flows. With that template in place, the place to start the assessment of corporate profitability is to to look at how much companies generated in each of the different earnings metrics around the world in 2024, broken down by sector: For the financial services sector, note that I have left revenues, gross profit, EBITDA and operating profit as not applicable, because of their unique structure, where debt is raw material and revenue is tough to nail down. (Conventional banks often start their income statements with net interest income, which is interest expense on their debt/deposits netted out against net income, making it closer to nough to categorize and compare to non-financial firms). I have also computed the percentage of firms globally that reported positive profits, a minimalist test on profitability in 2024, and there are interesting findings (albeit some not surprising) in this table: On a net profit basis, there is no contest for the sector that delivers the most net income. It is financials by a wide margin, accounting for a third of the net profits generated by all firms globally in 2024. In fact, technology, which is the sector with the highest market cap in 2024, is third on the list, with industrials taking second place. As you move from down the income statement, the percentage of firms that report negative earnings decreases. Across the globe, close to 84% of firms had positive gross profits, but that drops to 67% with EBITDA, 62% percent with operating income and 61% with net income. Across sectors, health care has the highest percentage of money-losing companies, on every single metric, followed by materials and communication services, whereas utilities had the highest percentage of money makers. While looking at dollar profits yields intriguing results, comparing them across sectors or regions is difficult to do, because they are in absolute terms, and the scale of businesses vary widely. The simple fix for that is to measure profitability relative to revenues, yielding profit margins - gross margins for gross profits, operating margins with operating profits and net margins with net profits. At the risk of stating these margins, not only are these margins not interchangeable, but they each convey information that is useful in understanding the economics of a business: As you can see, each of the margins provides insight (noisy, but still useful) about different aspects of a business model. With gross margins, you are getting a measure of unit economics, i.e., the cost of producing the next unit of sale. Thus, for a software company, this cost is low or even zero, but for a manufacturing company, no matter how efficient, the cost will be higher. Even within businesses that look similar, subtle differences in business models can translate into different unit economics. For Netflix, adding a subscriber entails very little in additional cost, but for Spotify, a company that pays for the music based on what customers listen to, by the stream, the additional subscriber will come with additional cost. Just to get a big picture perspective on unit economics, I ranked industries based upon gross margin and arrived at the following list of the ten industries with the highest gross margins and the ten with the lowest: With the caveat that accounting choices can affect these margins, you can see that the rankings do make intuitive sense. The list of industry groups that have the highest margins are disproportionately in technology, though infrastructure firms (oil and gas, green energy, telecom) also make the list since their investment is up front and not per added product sold. The list of industry group with the lowest margins are heavily tilted towards manufacturing and retail, the former because of the costs of making their products and the latter because of their intermediary status. With operating margins, you are getting a handle on economies of scale. While every companies claims economies of scale as a rationale for why margins should increase as they get larger, the truth is more nuanced. Economies of scale will be a contributor to improving margins only if a company has significant operating expenses (SG&A, Marketing) that grow at a rate lower than revenues. To measure the potential for economies of scale, I looked at the difference between gross and operating margins, across industries, with the rationale that companies with a large difference have a greater potential for economies of scale. Many of the industry groups in the lowest difference (between gross and operating margin) list were also on the low gross margin list, and the implication is not upbeat. When valuing or analyzing these firms, not only should you expect low margins, but those margins will not magically improve, just because a firm becomes bigger. The EBITDA margin is an intermediate stop, and it serves two purposes. If provides a ranking based upon operating cash flow, rather than operating earnings, and for businesses that have significant depreciation, that difference can be substantial. It is also a rough measure of capital intensity since to generate large depreciation/amortization, these companies also had to have substantial cap ex. Using the difference between EBITDA and operating margin as a measure of capital intensity, the following table lists the industries with the most and least capital intensity: Profit margins by industry: US, Global, Emerging Markets, Europe, Japan, India and China Again, there are few surprises on this list, including the presence of biotech at the top of the most capital intensive list, but that is due to the significant amortization line items on their balance sheets, perhaps from writing off failed R&D, and real estate on the top of the least capital intensive list, but the real estate segment in question is for real estate operations, not ownership. The net margin, in many ways, is the least informative of the profit margins, because there are so many wild cards at play, starting with differences in taxes (higher taxes lower net income), financial leverage (more leverage reduces net margins), cash holdings (interest from higher cash balances increases net income) and cross holdings (with varying effects depending on how they are accounted for, and whether they make or lose money). Ranking companies based upon net margin may measure everything from differences in financial leverage (more net debt should lead to lower margins) to extent of cross holdings and non-operating investments (more of these investments can lead to higher margins). Accounting Returns While scaling profits to revenues to get margins provides valuable information about business models and their efficacy, scaling profits to capital invested in a business is a useful tool for assessing the efficiency of capital allocation at the business., The two measures of profits from the previous section that are scaled to capital are operating income (before and after taxes) and net income, with the former measured against total invested capital (from equity and debt) and the latter against just equity capital. Using a financial balance sheet structure again, here is what we get: The achilles heel for accounting return measures is their almost total dependence on accounting numbers, with operating (net) income coming from income statements and invested capital (equity) from accounting balance sheets. Any systematic mistakes that accountants make (such as not treating leases as debt, which was the default until 2019, and treating R&D as an operating expense, which is still the case) will skew accounting returns. In addition, accounting decisions to write off an asset or take restructuring charges will make the calculation of invested capital more difficult. I wrote a long (and boring) paper on the mechanics of computing accounting returns laying out these and other challenges in computing accounting returns, and you are welcome to browse through it, if you want. If you are willing to live with the limitations, the accounting returns become proxies for what a business earns on its equity (with return on equity) and as a business (with the cost of capital). Since the essence of creating value is that you need to earn more than your cost of capital, you can synthesize returns with the costs of equity and capital that I talked about in the last post, to get measures of excess returns: I have the data to compute the accounting returns for the 48,000 publicly traded companies in my sample, though there are estimation choices that I had to make, when computing returns on equity and capital: Thus, you will note that I have bypassed accounting rules and capitalized R&D and leases (even in countries where it is not required) to come up with my versions of earnings and invested capital. Having computed the return on capital (equity) for each company, I then compared that return to the cost of capital (equity) to get a measure of excess returns for the company. In the table below, I start by breaking companies down by sector, and looking at the statistics on excess returns, by sector: Note that across all firms, only about 30% of firms earn a return on capital that exceeds the cost of capital. Removing money-losing firms, which have negative returns on capital from the sample, improves the statistic a little, but even across money making firms, roughly half of all firms earn less the the cost of capital.While the proportions of firms that earn returns that exceed the cost of equity (capital) vary across sectors, there is no sector where an overwhelming majority of firms earn excess returns. I disaggregate the sectors into industry groups and rank them based upon excess returns in the table below, with the subtext being that industries that earn well above their cost of capital are value creators (good businesses) and those that earn below are value destroyers (bad businesses): Excess returns by industry: US, Global, Emerging Markets, Europe, Japan, India and China There are some industry groups on this list that point to the weakness of using last year's earnings to get accounting return on capital. You will note that biotech drug companies post disastrously negative returns on capital but many of these firms are young firms, with some having little or no revenues, and their defense would be that the negative accounting returns reflect where they fall in the life cycle. Commodity companies cycle between the most negative and most returns lists, with earnings varying across the cycle; for these firms, using average return on capital over a longer period should provide more credible results. Finally, I look at excess returns earned by non-financial service companies by sub-region, again to see if companies in some parts of the world are better positioned to create value than others: As you can see, there is no part of the world that is immune from this problem, and only 29% of all firms globally earn more than their cost of capital. Even if you eliminate firms with negative earnings, the proportion of firms that earn more than their cost of capital is only 46.5%. Implications I have been doing versions of this table every year for the last decade, and the results you see in this year's table, i.e., that 70% of global companies generate returns on equity (capital) that are less tan their hurdle rates, has remained roughly static for that period. Making money is not enough for success: In many businesses, public or private, managers and even owners seem to think that making money (having a positive profit) represents success, not recognizing that the capital invested in these businesses could have been invested elsewhere to earn returns. Corporate governance is a necessity; Marty Lipton, a renowned corporate lawyer and critic of this things activist argued that activist investing was not necessary because most companies were well managed, and did not need prodding to make the right choices. The data in this post suggests otherwise, with most companies needing reminders from outside investors about the opportunity cost of capital. Companies are not fatted calves: In the last few years, two groups of people have targeted companies - politicians arguing that companies are price-gouging and the virtue crowd (ESG, sustainability and stakeholder wealth maximizers) pushing for companies to spend more on making the world a better place. Implicit in the arguments made by both groups is the assumption that companies are, at least collectively, are immensely profitable and that they can afford to share some of those spoils with other stakeholders (cutting prices for customers with the first group and spending lavishly on advancing social agendas with the second). That may be true for a subset of firms, but for most companies, making money has only become more difficult over the decades, and making enough money to cover the cost of the capital that they raise to create their businesses is an even harder reach. Asking these already stretched companies to spend more money to make the world a better place will only add to the likelihood that they will snap, under the pressures. A few months ago, I was asked to give testimony to a Canadian legislative committee that was planning to force Canadian banks to lend less to fossil fuel companies and more to green energy firms, a terrible idea that seems to have found traction in some circles. If you isolate the Canadian banks in the sample, they collectively generated returns on equity of 8.1%, with two thirds of banks earning less than their costs of equity. Pressuring these banks to lend less to their best customers (in terms of credit worthiness) and more to their worst customers (green energy company are, for the most part, financial basket cases) is a recipe for pushing these banks into distress, and most of the costs of that distress will be borne not by shareholders, but by bank depositors. YouTube Video Data Updates for 2025 Data Update 1 for 2025: The Draw (and Danger) of Data! Data Update 2 for 2025: The Party continued for US Equities Data Update 3 for 2025: The times they are a'changin'! Data Update 4 for 2025: Interest Rates, Inflation and Central Banks! Data Update 5 for 2025: It's a small world, after all! Data Update 6 for 2025: From Macro to Micro - The Hurdle Rate Question! Data Update 7 for 2025: The End Game in Business! Data Links Excess returns by industry: US, Global, Emerging Markets, Europe, Japan, India and China Profit margins by industry: US, Global, Emerging Markets, Europe, Japan, India and China Paper Links Return on Capital (ROC), Return on Invested Capital (ROIC) and Return on Equity: Measurement and Implications
In the first five posts, I have looked at the macro numbers that drive global markets, from interest rates to risk premiums, but it is not my preferred habitat. I spend most of my time in the far less rarefied air of corporate finance and valuation, where businesses try to decide what projects to invest in, and investors attempt to estimate business value. A key tool in both endeavors is a hurdle rate – a rate of return that you determine as your required return for business and investment decisions. In this post, I will drill down to what it is that determines the hurdle rate for a business, bringing in what business it is in, how much debt it is burdened with and what geographies it operates in. The Hurdle Rate - Intuition and Uses You don't need to complete a corporate finance or valuation class to encounter hurdle rates in practice, usually taking the form of costs of equity and capital, but taking a finance class both deepens the acquaintance and ruins it. It deepens the acquaintance because you encounter hurdle rates in almost every aspect of finance, and it ruins it, by making these hurdle rates all about equations and models. A few years ago, I wrote a paper for practitioners on the cost of capital, where I described the cost of capital as the Swiss Army knife of finance, because of its many uses. In my corporate finance class, where I look at the first principles of finance that govern how you run a business, the cost of capital shows up in every aspect of corporate financial analysis: In business investing (capital budgeting and acquisition) decisions, it becomes a hurdle rate for investing, where you use it to decide whether and what to invest in, based on what you can earn on an investment, relative to the hurdle rate. In this role, the cost of capital is an opportunity cost, measuring returns you can earn on investments on equivalent risk. In business financing decisions, the cost of capital becomes an optimizing tool, where businesses look for a mix of debt and equity that reduces the cost of capital, and where matching up the debt (in terms of currency and maturity) to the assets reduces default risk and the cost of capital. In this context, the cost of capital become a measure of the cost of funding a business: In dividend decisions, i.e., the decisions of how much cash to return to owners and in what form (dividends or buybacks), the cost of capital is a divining rod. If the investments that a business is looking at earn less than the cost of capital, it is a trigger for returning more cash, and whether it should be in the form of dividends or buybacks is largely a function of what shareholders in that company prefer: The end game in corporate finance is maximizing value, and in my valuation class, where I look at businesses from the outside (as a potential investor), the cost of capital reappears again as the risk-adjusted discount rate that you use estimate the intrinsic value of a business. Much of the confusion in applying cost of capital comes from not recognizing that it morphs, depending on where it is being used. An investor looking at a company, looking at valuing the company, may attach one cost of capital to value the company, but within a company, but within a company, it may start as a funding cost, as the company seeks capital to fund its business, but when looking at investment, it becomes an opportunity cost, reflecting the risk of the investment being considered. The Hurdle Rate - Ingredients If the cost of capital is a driver of so much of what we do in corporate finance and valuation, it stands to reason that we should be clear about the ingredients that go into it. Using one of my favored structures for understanding financial decision making, a financial balance sheet, a cost of capital is composed of the cost of equity and the cost of debt, and I try to capture the essence of what we are trying to estimate with each one in the picture below: To go from abstractions about equity risk and default risk to actual costs, you have to break down the costs of equity and debt into parts, and I try to do so, in the picture below, with the factors that you underlie each piece: As you can see, most of the items in these calculations should be familiar, if you have read my first five data posts, since they are macro variables, having nothing to do with individual companies. The first is, of course, the riskfree rate, a number that varies across time (as you saw in post on US treasury rates in data update 4) and across currencies (in my post on currencies in data update 5). The second set of inputs are prices of risk, in both the equity and debt markets, with the former measured by equity risk premiums, and the latter by default spreads. In data update 2, I looked at equity risk premiums in the United States, and expanded that discussion to equity risk premiums in the rest of the world in data update 5). In data update 4, I looked at movements in corporate default spreads during 2024. There are three company-specific numbers that enter the calculation, all of which contribute to costs of capital varying across companies; Relative Equity Risk, i.e., a measure of how risky a company's equity is, relative to the average company's equity. While much of the discussion of this measure gets mired in the capital asset pricing model, and the supposed adequacies and inadequacies of beta, I think that too much is made of it, and that the model is adaptable enough to allow for other measures of relative risk. I am not a purist on this measure, and while I use betas in my computations, I am open to using alternate measures of relative equity risk. Corporate Default Risk, i.e, a measure of how much default risk there is in a company, with higher default risk translating into higher default spreads. For a fairly large subset of firms, a bond rating may stand in as this measure, but even in its absence, you have no choice but to estimate default risk. Adding to the estimation challenge is the fact that as a company borrows more money, it will play out in the default risk (increasing it), with consequences for both the cost of equity and debt (increasing both of those as well). Operating geographies: The equity risk premium for a company does not come from where it is incorporated but from where it does business, both in terms of the production of its products and services and where it generates revenue. That said, the status quo in valuation in much of the world seems to be to base the equity risk premium entirely on the country of incorporation, and I vehemently disagree with that practice: Again, I am flexible in how operating risk exposure is measured, basing it entirely on revenues for consumer product and business service companies, entirely on production for natural resource companies and a mix of revenues and production for manufacturing companies. As you can see, the elements that go into a cost of capital are dynamic and subjective, in the sense that there can be differences in how one goes about estimating them, but they cannot be figments of your imagination. The Hurdle Rate - Estimation in 2025 With that long lead in, I will lay out the estimation choices I used to estimate the costs of equity, debt and capital for the close to 48,000 firms in my sample. In making these choices, I operated under the obvious constraint of the raw data that I had on individual companies and the ease with which I could convert that data into cost of capital inputs. Riskfree rate: To allow for comparisons and consolidation across companies that operate in different currencies, I chose to estimate the costs of capital for all companies in US dollars, with the US ten-year treasury rate on January 1, 2025, as the riskfree rate. Equity Risk Premium: Much as I would have liked to compute the equity risk premium for every company, based upon its geographic operating exposure, the raw data did not lend itself easily to the computation. Consequently, I have used the equity risk premium of the country in which a company is headquartered to compute the equity risk premium for it. Relative Equity Risk: I stay with beta, notwithstanding the criticism of its effectiveness for two reasons. First, I use industry average betas, adjusted for leverage, rather than the company regression beta, because because the averages (I title them bottom up betas) are significantly better at explaining differences in returns across stocks. Second, and given my choice of industry average betas, none of the other relative risk measures come close, in terms of predictive ability. For individual companies, I do use the beta of their primary business as the beta of the company, because the raw data that I have does not allow for a breakdown into businesses. Corporate default risk: For the subset of the sample of companies with bond ratings, I use the S&P bond rating for the company to estimate the cost of debt. For the remaining companies, I use interest coverage ratios as a first measure to estimate synthetic ratings, and standard deviation in stock prices as back-up measure. Debt mix: I used the market capitalization to measure the market value of equity, and stayed with total debt (including lease debt) to estimate debt to capital and debt to equity ratios The picture below summarizes my choices: There are clearly approximations that I used in computing these global costs of capital that I would not use if I were computing a cost of capital for valuing an individual company, but this approach yields values that can yield valuable insights, especially when aggregated and averaged across groups. a. Sectors and Industries The risks of operating a business will vary widely across different sectors, and I will start by looking at the resulting differences in cost of capital, across sectors, for global companies: There are few surprises here, with technology companies facing the highest costs of capital and financials the lowest, with the former pushed up by high operating risk and a resulting reliance on equity for capital, and the latter holding on because of regulatory protection. Broken down into industries, and ranking industries from highest to lowest costs of capital, here is the list that emerges: Download industry costs of capital The numbers in these tables may be what you would expect to see, but there are a couple of powerful lessons in there that businesses ignore at their own peril. The first is that even a casual perusal of differences in costs of capital across industries indicates that they are highest in businesses with high growth potential and lowest in mature or declining businesses, bringing home again the linkage between danger and opportunity. The second is that multi-business companies should understand that the cost of capital will vary across businesses, and using one corporate cost of capital for all of them is a recipe for cross subsidization and value destruction. b. Small versus Larger firms In my third data update for this year, I took a brief look at the small cap premium, i.e, the premium that small cap stocks have historically earned over large cap stocks of equivalent risk, and commented on its disappearance over the last four decades. I heard from a few small cap investors, who argued that small cap stocks are riskier than large cap stocks, and should earn higher returns to compensate for that risk. Perhaps, but that has no bearing on whether there is a small cap premium, since the premium is a return earned over and above what you would expect to earn given risk, but I remained curious as to whether the conventional wisdom that small cap companies face higher hurdle rates is true. To answer this question, I examine the relationship between risk and market cap, breaking companies down into market cap deciles at the start of 2025, and estimating the cost of capital for companies within each decile: The results are mixed. Looking at the median costs of capital, there is no detectable pattern in the cost of capital, and the companies in the bottom decile have a lower median cost of capital (8.88%) than the median company in the sample (9.06%). That said, the safest companies in largest market cap decile have lower costs of capital than the safest companies in the smaller market capitalizations. As a generalization, if small companies are at a disadvantage when they compete against larger companies, that disadvantage is more likely to manifest in difficulties growing and a higher operating cost structure, not in a higher hurdle rate. c. Global Distribution In the final part of this analysis, I looked at the costs of capital of all publicly traded firms and played some Moneyball, looking at the distribution of costs of capital across all firms. In the graph below,I present the histogram of cost of capital, in US dollar terms, of all global companies at the start of 2025, with a breakdown of costs of capital, by region, below: I find this table to be one of the most useful pieces of data that I possess and I use it in almost every aspect of corporate finance and valuation: Cost of capital calculation: The full cost of capital calculation is not complex, but it does require inputs about operating risk, leverage and default risk that can be hard to estimate or assess for young companies or companies with little history (operating and market). For those companies, I often use the distribution to estimate the cost of capital to use in valuing the company. Thus, when I valued Uber in June 2014, I used the cost of capital (12%) at the 90th percentile of US companies, in 2014, as Uber's cost of capital. Not only did that remove a time consuming task from my to-do list, but it also allowed me to focus on the much more important questions of revenue growth and margins for a young company. Drawing on my fifth data update, where I talk about differences across currencies, this table can be easily modified into the currency of your choice, by adding differential inflation. Thus, if you are valuing an Indian IPO, in rupees, and you believe it is risky, at the start of 2025, adding an extra 2% (for the inflation differential between rupees and dollars in 2025) to the ninth decile of Indian costs of capital (12.08% in US dollars) will give you a 14.08% Indian rupee cost of capital. Fantasy hurdle rates: In my experience, many investors and companies make up hurdle rates, the former to value companies and the latter to use in investment analysis. These hurdle rates are either hopeful thinking on the part of investors who want to make that return or reflect inertia, where they were set in stone decades ago and have never been revisited. In the context of checking to see whether a valuation passes the 3P test (Is it possible? Is it plausible? Is it probable?), I do check the cost of capital used in the valuation. A valuation in January 2025, in US dollars, that uses a 15% cost of capital for a publicly traded company that is mature is fantasy (since it is in well in excess of the 90th percentile), and the rest of the valuation becomes moot. Time-varying hurdle rates: When valuing companies, I believe in maintaining consistency, and one of the places I would expect it to show up is in hurdle rates that change over time, as the company's story changes. Thus, if you are valuing a money-losing and high growth company, you would expect its cost of capital to be high, at the start of the valuation, but as you build in expectations of lower growth and profitability in future years, I would expect the hurdle rate to decrease (from close to the ninth decile in the table above towards the median). It is worth emphasizing that since my riskfree rate is always the current rate, and my equity risk premiums are implied, i.e., they are backed out from how stocks are priced, my estimates of costs of capital represent market prices for risk, not theoretical models. Thus, if looking at the table, you decide that a number (median for your region, 90th percentile in US) look too low or too high, your issues are with the market, not with me (or my assumptions). Takeaways I am sorry that this post has gone on as long as it has, but to end, there are four takeaways from looking at the data: Corporate hurdle rate: The notion that there is a corporate hurdle rate that can be used to assess investments across the company is a myth, and one with dangerous consequences. It plays out in all divisions in a multi-business company using the same (corporate) cost of capital and in acquisitions, where the acquiring firm's cost of capital is used to value the target firm. The consequences are predictable and damaging, since with this practice, safe businesses will subsidize risky businesses, and over time, making the company riskier and worse off over time. Reality check on hurdle rates: All too often, I have heard CFOs of companies, when confronted with a cost of capital calculated using market risk parameters and the company's risk profile, say that it looks too low, especially in the decade of low interest rates, or sometimes, too high, especially if they operate in an risky, high-interest rate environment. As I noted in the last section, making up hurdle rates (higher or lower than the market-conscious number) is almost never a good idea, since it violates the principle that you have live and operate in the world/market you are in, not the one you wished you were in. Hurdle rates are dynamic: In both corporate and investment settings, there is this almost desperate desire for stability in hurdle rates. I understand the pull of stability, since it is easier to run a business when hurdle rates are not volatile, but again, the market acts as a reality check. In a world of volatile interest rates and risk premia, using a cost of capital that is a constant is a sign of denial. Hurdle rates are not where business/valuation battles are won or lost: It is true that costs of capital are the D in a DCF, but they are not and should never be what makes or breaks a valuation. In my four decades of valuation, I have been badly mistaken many times, and the culprit almost always has been an error on forecasting growth, profitability or reinvestment (all of which lead into the cash flows), not the discount rate. In the same vein, I cannot think of a single great company that got to greatness because of its skill in finessing its cost of capital, and I know of plenty that are worth trillions of dollars, in spite of never having actively thought about how to optimize their costs of capital. It follows that if you are spending the bulk of your time in a capital budgeting or a valuation, estimating discount rates and debating risk premiums or betas, you have lost the script. If you are valuing a mature US company at the start of 2025, and you are in a hurry (and who isn't?), you would be well served using a cost of capital of 8.35% (the median for US companies at the start of 2025) and spending your time assessing its growth and profit prospects, and coming back to tweak the cost of capital at the end, if you have the time. YouTube Video Data Updates for 2025 Data Update 1 for 2025: The Draw (and Danger) of Data! Data Update 2 for 2025: The Party continued for US Equities Data Update 3 for 2025: The times they are a'changin'! Data Update 4 for 2025: Interest Rates, Inflation and Central Banks! Data Update 5 for 2025: It's a small world, after all! Data Update 6 for 2025: From Macro to Micro - The Hurdle Rate Question! Data Links Cost of capital, by industry grouping: US, Global, Emerging Markets, Japan, Europe, India, China) Cost of capital distribution, by industry Paper links The Cost of Capital: The Swiss Army Knife of Finance
If the title of this post sounds familiar, it is because is one of Disney’s most iconic rides, one that I have taken hundreds of times, first with my own children and more recently, with my grandchildren. It is a mainstay of every Disney theme park, from the original Disneyland in Anaheim to the newer theme parks in Paris, Hong Kong and Shanghai. For those of who have never been on it, it is the favored ride for anyone who is younger than five in your group, since you spend ten minutes in a boat going through the world as Disney would like you to see it, full of peace, happiness, and goodwill. In this post, I will expand my analysis of data in 2024, which has a been mostly US-centric in the first four of my posts, and use that data to take you on my version of the Disney ride, but on this trip, I have no choice but to face the world as is, with all of the chaos it includes, with tariffs and trade wars looming. Returns in 2024 Clearly, the most obvious place to start this post is with market performance, and in the table below, I report the percentage change in index level, for a subset of indices, in 2024: The best performing index in 2024, at least for the subset of indices that I looked at, was the Merval, up more than 170% in 2024, and that European indices lagged the US in 2024. The Indian and Chinese markets cooled off in 2024, posting single digit gains in price appreciation. There are three problems with comparing returns in indices. First, they are indices and reflect a subset of stocks in each market, with different criteria determining how each index is constructed, and varying numbers of constituents. Second, they are in local currencies, and in nominal terms. Thus, the 172.52% return in the Merval becomes less impressive when inflation in Argentina is taken into account. It is for this reason that I chose to compute returns differently, using the following constructs: I included all publicly traded stocks in each market, or at least those with a market capitalization available for them. I converted all of the market capitalizations into US dollars, just to make them comparable. I aggregated the market capitalizations of all stocks at the end of 2023 and the end of 2024, and computed the percentage change. The results, broken down broadly by geography are in the table below: As you can see, the aggregate market cap globally was up 12.17%, but much of that was the result of a strong US equity market. Continuing a trend that has stretched over the last two decades, investors who tried to globally diversify in 2024 underperformed investors who stayed invested only in the United States. I do have the percentage changes in market cap, by country, but you should take those results with a grain of salt, since there are countries with just a handful of listings, where the returns are distorted. Looking at countries with at least ten company listings, I have a list of the ten best and worst performing countries in 2024: Argentina's returns in US dollar terms is still high enough to put it on top of the list of best-performing countries in the world in 2024 and Brazil is at the top of the list of worst performing countries, at least in US dollar terms. The Currency Effect As you can see comparing the local index and dollar returns, the two diverge in some parts of the world, and the reason for the divergence is movements in exchange rates. To cast light on this divergence, I looked at the US dollar's movements against other currencies, using three variants of US dollar indices against emerging market currencies, developed market currencies and broadly against all currencies: FRED The dollar strengthened during 2024, more (10.31%) against emerging market currencies than against developed market currencies (7.66%), and it was up broadly (9.03%). I am no expert on exchange rates, but learning to deal with different currencies in valuation is a prerequisite to valuing companies. Since I value companies in local currencies, I am faced with the task of estimating risk free rates in dozens of currencies, and the difficulty you face in estimating these rates can vary widely (and be close to impossible in some) across currencies. In general, you can break down risk free estimation, in different currencies, in three groupings, from easiest to most difficult: My process for estimating riskfree rates in a currency starts with a government issuing a long term bond in that currency, and if the government in question has no default risk, it stops there. Thus, the current market interest rate on a long term Swiss government bond, in Swiss Francs, is the risfree rate in that currency. The process gets messier, when there is a long-term, local currency bond that is traded, but the government issuing the bond has default risk. In that case, the default spread on the bond will have to be netted out to get to a riskfree rate in the currency. There are two key estimation questions that are embedded in this approach to estimating riskfree rates. The first is the assessment of whether there is default risk in a government, and I use a simplistic (and flawed) approach, letting the local currency sovereign rating for the government stand in as the measure; I assume that AAA rated government bonds are default-free, and that any rating below is a indication of default risk. The second is the estimation of the default spread, and in my simplistic approach, I use one of two approaches - a default spread based upon the sovereign rating or a sovereign credit default swap spread. At the start of 2025, there were just about three dozen currencies, where I was able to find local-currency government bonds, and I estimated the riskfree rates in these currencies; Download data At the risk of stating the obvious (and repeating what I have said in earlier posts), there is no such thing as a global riskfree rate, since riskfree rates go with currencies, and riskfree rates vary across currencies, with all or most of the difference attributable to differences in expected inflation. High inflation currencies will have high riskfree rates, low inflation currencies low riskfree rates and deflationary currencies can negative riskfree rates. It is the recognition that differences in riskfree rates are primarily due to differences in expected inflation that gives us an opening to estimate riskfree rates in currencies without a government bond rate, or even to run a sanity check on the riskfree rates that you get from government bonds. If you start with a riskfree rate in a currency where you can estimate it (say US dollars, Swiss Francs or Euros), all you need to estimate a riskfree rate in another currency is the differential inflation between the two currencies. Thus, if the US treasury bond rate (4.5%) is the riskfree rate in US dollars, and the expected inflation rates in US dollars and Brazilian reals are 2.5% and 7.5% respectively, the riskier rate in Brazilian reals: Riskfree rate in $R = (1+ US 10-year T.Bond Rate) * (1 + Expected inflation rate in $R)/ (1+ Expected inflation rate in US $) - 1 = 1.045 *(1.075/1.025) -1 = 9.60% In approximate terms, this can be written as Riskfree rate in $R = US 10-year T.Bond Rate + (Expected inflation rate in $R) - Expected inflation rate in US $) - 1 = 4.5% - (7.5% - 2.5%) = 9.50% While obtaining an expected inflation rate for the US dollar is easy (you can use the difference between the ten-year US treasury bond rate and the ten-year US TIPs rate), it can be more difficult to obtain this number in Egyptian pounds or in Zimbabwean dollars, but you can get estimates from the IMF or the World Bank. The Risk Effect There are emerging markets that have delivered higher returns than developed markets, but in keeping with a core truth in investing and business, these higher returns often go hand-in-hand with higher risk. The logical step in looking across countries is measuring risk in countries, and bringing that risk into your analysis, by incorporating that risk by demanding higher expected returns in riskier countries. That process of risk analysis and estimating risk premiums starts by understanding why some countries are riskier than others. The answers, to you, may seem obvious, but I find it useful to organize the obvious into buckets for analysis. I will use a picture in posts on country risk before to capture the multitude of factors that go into making some countries riskier than others: To get from these abstractions to country risk measures, I make a lot of compromises, putting pragmatism over purity. While I take a deeper look at the different components of country risk in my annual updates on country risk (with the most recent one from 2024), I will cut to the chase and focus explicitly on my approach to estimating equity risk premiums, using my 2025 data update to illustrate: With this approach, I estimated equity risk premiums, by country, and organized by region, here is what the world looked like, at the start of 2025: Download equity risk premiums by country Note that I attach the implied equity risk premium for the S&P 500 of 4.33% (see my data update 3 from a couple of weeks ago) to all Aaa rated countries (Australia, Canada, Germany etc.) and an augmented premium for countries that do not have Aaa ratings, with the additional country risk premium determined by local currency sovereign ratings. I am aware of all of the possible flaws in this approach. First, treating the US as default-free is questionable, now that it has threatened default multiple times in the last decade and has lost its Aaa rating with every ratings agency, other than Moody's. That is an easily fixable problem, though, since if you decide to use S&P's AA+ rating for the US, all it would require is that you net out the default spread of 0.40% (for a AA+ rating at the start of 2025) from the US ERP to get a mature market premium of 3.93% (4.33% minus 0.40%). Second, ratings agencies are not always the best assessors of default risk, especially when there are dramatic changes in a country, or when they are biased (towards or against a region). That too has a fix, at least for the roughly 80 countries where there are trade sovereign CDS spreads, and those sovereign CDS spreads can be used instead of the ratings-based spreads for those countries. The Pricing Effect As an investor, the discussions about past returns and risk may miss the key question in investing, which is pricing. At the right price, you should be willing to buy stocks even in the riskiest countries, and especially so after turbulent (down) years. At the wrong price, even the safest market with great historical returns are bad investments. To assess pricing in markets, you have to scale the market cap to operating metrics, i.e., estimate a multiple, and while easy enough to do, there are some simple rules to follow in pricing. The first is recognizing that every multiple has a market estimate of value in the numerator, capturing either just equity value (market cap of equity), total firm value (market cap of equity + total debt) or operating asset (enterprise) value (market cap of equity + total debt - cash): Depending on the scalar (revenues, earnings, book value or cash flow), you can compute a variety of multiples, and if you add on the choices on timing for the scaling variables (trailing, current, forward), the choices multiply. To the question of which multiple is best, a much debated topic among analysts, my answer is ambivalent, since you can use any of them in pricing, as long as you ask the right follow-up questions. To compare how stocks are priced globally, I will use three of these multiples. The first is the price earnings ratio, partly because in spite of all of its faults, it remains the most widely used pricing metric in the world. The second is the polar opposite on the pricing spectrum, which is the enterprise value to sales multiple, where rather than focus on just equity value, I look at operating asset value, and scale it to the broadest of operating metrics, which is revenue. While it takes a lot to get from revenues to earnings, the advantage of using revenues is that it is number least susceptible to accounting gaming, and also the one where you are least likely to lose companies from your sample. (Thousands and thousands of companies in my sample have negative net income, making trailing PE not meaningful, but very few (usually financial service firms) have missing revenues). The third pricing metric I look at is the enterprise value to EBITDA, a multiple that has gone from being lightly used four decades ago to a banking punchline today, where EBITDA represents a rough measure of operating cash flow). With each of these multiples, I make two estimation choices: I stay with trailing values for net income, revenues and EBITDA, because too many of the firms in my 48,000 firm sample have no analysts following them, and hence no forward numbers. I compute two values for each country (region), an aggregated version and the median value. While the latter is simple, i.e., it is the median number across all companies in a country or region, the former is calculated across all companies, by aggregating the values across companies. Thus, the aggregated PE ratio for the United States is 20.51, and it computed by adding up the market capitalizations of all traded US stocks and dividing by the sum of the net income earned by all traded firms, including money losers. Think of it a weighted-average PE, with no sampling bias. With these rules in place, here is what the pricing metrics looked like, by region, at the start of 2025: The perils of investing based just upon pricing ratios should be visible from this table. Two of the cheapest regions of the world to invest in are Latin America and Eastern Europe, but both carry significant risk with them, and the third, Japan, has an aging population and is a low-growth market. The most expensive market in the world is India, and no amount of handwaving about the India story can justify paying 31 times earnings, 3 times revenue and 20 times EBITDA, in the aggregate, for Indian companies. The US and China also fall into the expensive category, trading at much higher levels than the rest of the world, on all three pricing metrics. Within each of these regions, there are differences across countries, with some priced more richly than others. In the table below, I look at the ten countries, with at least 5 companies listed on their exchanges, that trade at the lowest median trailing PE ratios, and the ten countries that are more expensive using that same metric: Many of the markets are in the world that trade at the lowest multiples of trailing earnings are in Africa. With Latin America, it is a split decisions, where you have two countries (Colombia and Brazil) on the lowest PE list and one (Argentina) on the highest PE list. In some of the countries, there is a divergence between the aggregated version and the trailing PE, with the aggregated PE higher (lower) than the median value, reflecting larger companies that trade at lower (higher) PE ratios than the rest of the market. Replacing market cap with enterprise value, and net income with revenues, gives you a pricing multiple that lies at the other end of the spectrum, and ranking countries again, based on median EV to sales multiples, here is the list of the ten most expensive and cheapest markets: On an enterprise value to sales basis, you see a couple of Asian countries (Japan and South Korea) make the ten lowest list, but the preponderance of Middle Eastern countries on ten highest lists may just be a reflection of quirks in sample composition (more financial service firms, which have no revenues, in the sample). The Year to come This week has been a rocky one for global equities, and the trigger for the chaos has come from the United States. The announcements, from the Trump administration, of the intent to impose 25% tariffs on Canada and Mexico may have been delayed, and perhaps may not even come into effect, but it seems, at least to me, a signal that globalization, unstoppable for much of the last four decades, has crested, and that nationalism, in politics and economics, is reemerging. As macroeconomists are quick to point out, using the Great Depression and Smoot-Hawley's tariffs in the 1930 to illustrate, tariffs are generally not conducive to global economic health, but it is time that they took some responsibility for the backlash against free global trade and commerce. After all, the notion that globalization was good for everyone was sold shamelessly, even though globalization created winners (cities, financial service firms) and losers (urban areas, developed market manufacturing) , and much of what we have seen transpired over the last decade (from Brexit to Trump) can be viewed as part of the backlash. In spite of the purse clutching at the mention of tariffs, they have been part of global trade as long as there has been trade, and they did not go away after the experiences with the depression. I agree that the end game, if tariffs and trade wars become commonplace, will be a less vibrant global economy, but as with any major macroeconomic shocky, there will be winners and losers. There is, I am sure, a sense of schadenfreude among many in emerging markets, as they watch developed markets start to exhibit the behavior (unpredictable government policy, subservient central banks, breaking of legal and political norms) that emerging markets were critiqued for decades ago, but the truth is that the line between developed and emerging markets has become a hazy one. After the fall of the Iron Curtain, George H.W. Bush (the senior) declared a "new world order", a proclamation turned out to be premature, since the old world order quickly reasserted itself. The political and economic developments of the last decade may signal the arrival of a new world order, though no one in quite sure whether it will be better or worse than the old one. YouTube Video Data Updates for 2025 Data Update 1 for 2025: The Draw (and Danger) of Data! Data Update 2 for 2025: The Party continued for US Equities Data Update 3 for 2025: The times they are a'changin'! Data Update 4 for 2025: Interest Rates, Inflation and Central Banks! Data Update 5 for 2025: It's a small world, after all! Data Links Riskfree rates, by currency, in January 2025 Equity risk premiums, by country, in January 2025 Pricing ratios, by country, in January 2025
I am going to start this post with a confession that my knowledge of the architecture and mechanics of AI are pedestrian and that there will be things that I don't get right in this post. That said, DeepSeek's abrupt entry into the AI conversation has the potential to change the AI narrative, and as it does, it may also change the storylines for the many companies that have spent the last two years benefiting from the AI hype. I first posted about AI in the context of valuing Nvidia, in June 2023, when there was still uncertainty about whether AI had legs. A little over a year later, in September 2024, that question about AI seemed to have been answered in the affirmative, for most investors, and I posted again after Nvidia had a disappointing earnings report, arguing that it reflected a healthy scaling down of expectations. As talk of AI disrupting jobs and careers also picked up, I also posted a piece on the threat that AI poses for all of us, with its capacity to do our jobs, at low or no cost, and what I saw as the edges I could use to keep my bot at bay. For those of you who have been tracking the market, the AI segment in the market has held its own since September, but even before the last weekend, there were signs that investors were sobering up on not only how big the payoff to AI would be, but how long they would have to wait to get there. The AI story, before DeepSeek The AI story has been building for a while, reflecting the convergence of two forces in technology - more computing power, often in smaller and smaller packages, and the accumulation of data, on technology platforms and elsewhere. That said, the AI story broke out to the public on November 30, 2022, when OpenAI launched ChatGPT, and it made its presence felt in homes, schools and businesses almost instantaneously. It is that wide presence in our daily lives that laid the foundations for the AI story, where evangelists sold us on the notion that AI solutions would make our lives easier and take away the portions of our work that we found most burdensome, and that the businesses that provided these solutions would be worth trillions of dollars. As the number of potential applications of AI proliferated, thus increasing the market for AI products and services, another part of the story was also being put into play. AI was framed as being made possible by the marriage of incredibly powerful computers and deep troves of data, effectively setting the stage for the winners, losers, and wannabes in the story. The first set of companies were perceived as benefiting from building the AI architecture, with the advance spending on this architecture coming from the companies that hoped to be players in the AI product and service markets: Computing Power: In the AI story that was told, the computers that were needed were so powerful that they needed customized chips, more powerful and compact than any made before, and one company (Nvidia), by virtue of its early start and superior chip design capabilities, stood well above the rest. Not only did Nvidia have an 80% market share of the AI chip market, as assessed in 2024, the lead and first-mover advantage that the company possessed would give it a dominant market share, in the much larger AI chip market of the future. Along the way, the the AI story picked up supercomputing companies, as passengers, again on the belief that Ai systems would find a use for them. Power: In the AI story, the coupling of powerful computing and immense data happens in data centers that are power hogs, requiring immense amounts of energy to keep going. Not surprisingly, a whole host of power companies have stepped into the breach, with some increasing capacity entirely to service these data centers. Some of them were new entrants (like Constellation Energy), whereas others were more traditional power companies (Siemens Energy) who saw an opening for growth and profitability in the AI space. Data: A third beneficiary from the architecture part of the AI story were the cloud businesses, where the big data, collected for the AI systems would get stored. The big tech companies with cloud arms, particularly Microsoft (Azure) and Amazon (AWS) have benefited from that demand, as have other cloud businesses. Since the companies involved in building the AI infrastructure are the ones that are most tangibly (and immediately) benefiting from the AI boom, they are also the companies that have seen the biggest boost in market cap, as the AI story heated up. In the graph, I have picked on a subset of high-profile companies that were part of the AI market euphoria and looked at the consequent increase in their market capitalizations: Using the ChatGPT introduction on November 30, 2022, as the starting point for the AI buzz, in public consciousness and markets, the returns in 2023 and 2024 are a composite (albeit a rough) measure of the benefits that AI has generated for these companies. Note that the biggest percentage winner, at least in this group was Palantir, up 1285% in the last two years, but the biggest winner in absolute terms was Nvidia, which gained almost $ 3 trillion in value in 2023 and 2024. The investments in that AI architecture were being made, with the expectation that companies that invested in the architecture would be able to eventually profit from developing and selling AI products and services. Since the AI storyline required immense upfront investing in computing power and access to big data, the biggest investors in AI architecture were big tech companies, with Microsoft and Meta being the largest customers for Nvidia chips in 2024. In the table below, I look at the Mag Seven, not inclusive of Nvidia, and examine the returns that they have made in 2023 and 2024: As you can see, the Mag Seven carried the market in the two years, each adding a trillion (or close, in the case of Tesla) dollars in value in the last two years, with some portion of that value attributable to the AI story. With requirements for large investment up front acting as entry barriers, the expectation was these big tech companies would eventually not only be able to develop AI products and services that their customers would want, but charge premium prices (and earn higher margins). In the picture below, I have tried to capture the essence of AI story, with the potential winners and losers at each stage: There are parts to this story where there is much to be proved, especially on the AI product and service part, and while investors can be accused of becoming excessively exuberant about the story, it is a plausible one. In fact, my most recent (in September 2024) valuation of Nvidia bought into core elements of the story, though I still found it overvalued: Nvidia valuation in September 2024 (Pre DeepSeek) Note that the big AI story plays out in these inputs in multiple places: AI chip market: My September 2024 estimate for the size of the AI chip market was $500 billion, which in turn was justifiable only because the AI product and service market was expected to huge ($3 trillion and beyond). Nvidia market share: In my valuation, I assumed that Nvidia's lead in the AI chip business would give the company a head start, as the business grew, and to the extent that demand is sticky (i.e., once companies start build data centers with Nvidia chips, it would be difficult for them to switch to a competitor), Nvidia would maintain a dominant market share (60%) of the expanded AI chip market. Nvidia margins: Nvidia has had immense pricing power, posting nosebleed-level gross and operating margins, while TSMC (its chip maker) has generated only a fraction of the benefits, and its biggest customers (the big tech companies) have been willing to pay premium prices to get a head start in building their AI architecture. Over time, I assumed that Nvidia would see its margins drop, but even with the drop, their target margin (60%) would resemble those of very successful, software companies, not chip making companies. My concern in September 2024, and in fact for the bulk of the last two years, was not that I had doubts about the core AI story, but that investors were overpaying for the story. That is partly why, I have shed portions of my holdings in Nvidia, selling half my holdings in the summer of 2023 and another quarter in the summer of 2024. The AI Story, after DeepSeek I teach valuation, and have done so for close to forty years. One reason I enjoy the class is that you are never quite done with a valuation, because life keeps throwing surprises at you. The first session of my undergraduate valuation class was last Wednesday (January 22), and during the course of the class, I talked about how a good valuation connects narrative to numbers, and followed up by noting that even the most well thought through narratives will change over time. I am not sure how much of that message got through to my studentls, but the message was delivered much more effectively by DeepSeek's entry into the AI story over the weekend, and the market shakeup that followed when markets opened on Monday (January 27). A DeepSeek Primer The DeepSeek story is still being told, and there is much we do not know. For the moment, though, here is what we know. In 2010, Liang Wenfeng, a software engineer, founded DeepSeek as a hedge fund in China, with the intent of using artificial intelligence to make money. Unable to get traction in that endeavor, and facing government hostility on speculative trading, he pivoted in 2023 into AI, putting together a team to create a Chinese competitor to OpenAI. Since the intent was to come up with a product that could be sold at bargain prices, DeepSeek did what disruptors have always done, which is look for an alternate path to the same destination (providing AI products that work). Rather than invest in expensive infrastructure (supercomputers and data centers), DeepSeek used much cheaper, less powerful chips, and instead of using immense amounts of data, created an AI prototype that could work with less data, using rule-based logic to fill in the gap. While there has been chatter about DeepSeek for weeks, it became publicly accessible at the end of last week (ending January 24), and within hours, was drawing rave reviews from people well versed in tech, as it matched beat ChatGPT at many tasks, and even performed better on scientific and math queries. There are parts of this story that are clearly for public consumption, more side stories than main story,, and it is best to get them out of the way, before looking at the DeepSeek effect. Cost of development: The notion that DeepSeek was developed for just a few million dollars is fantasy, and while there may have been a portion of the development that cost little, the total was probably in the hundreds of millions of dollars and required a lot more resources (including perhaps even Nvidia chips) than the developers are letting on. No matter what the true cost of development is finally revealed to be, it will be a fraction of the money spent by the existing players in building their systems. Performance tests: The tests of DeepSeek versus OpenAI (or Claude and Gemini) suggests that DeepSeek not only holds it own against the establishment, but even outperforms them on some tasks. That is impressive, but the leap that some are making to concede the entire AI product and service market to DeepSeek is unwarranted. There are clearly aspects of the AI products and service business, where the DeepSeek approach (of using less powerful computing and data) will be good enough, but there will be other aspects of the AI business, where the old paradigm of super computing power and vast data will still hold. A Chinese company: The fact that DeepSeek was developed in China throws a political twist into the story that will undoubtedly play a role in how it develops, but the genie is out of the bottle, even if other governments try to stop its adoption. Adding to the noise is the decision by the company to make DeepSeek open-source, effectively allowing others to adapt and build their own versions. Fair or foul: Finally, there has been some news on the legal front, where OpenAI has argued that DeepSeek unlawfully used data that was generated by OpenAI in building their offering, and while part of that lawsuit may just be showboating, it is possible that portions of the story are true and that legal consequences will follow. While we can debate the what's and why's in this story, the market reaction this week to the story has been swift and decisive. I graph the performance of the five AI stocks highlighted in the earlier section, throwing in the Meta and Microsoft for good measure, on a daily basis in 2025. As you can see in this chart, Nvidia Broadcom, Constellation and Vistra have had terrible weeks, losing more than 10% in the last week, but just for perspective, also note that Constellation and Vistra are still up strongly for the year. Meta and Microsoft were unaffected, and so was Palantir, Clearly, the DeepSeek story is playing out differently for different companies in the AI space, but its overall market impact has been substantial, and for the most part, negative. What is it that makes the DeepSeek story so compelling? First, is the technological aspect of coming up with a product, with far less in resources that the establishment, and I have nothing but admiration for the DeepSeek creators, but the part of the story that stands out is that the they chose not to go with the prevailing narrative (the one where Nvidia chips and huge data bases are a necessity) and instead asked the question of what the end products and services would look like, and whether there was an easier, quicker and cheaper way of getting there. In hindsight, there are probably others who are looking at DeepSeek and wondering why they did not choose the same path, and the answer is that it takes courage to go against the conventional wisdom, especially when, as AI did over the last two years, it sweeps everyone (from tech titans to individual investors) along with its force. The truth is that even if DeepSeek is stopped through legal or government action or fails to deliver on its promises, what its entry has done to the AI story cannot be undone, since it has broken the prevailing narrative. I would not be surprised if there are a dozen other start-ups, right now, using the DeepSeek playbook to come up with their own lower-cost competitors to prevailing players. Put simply, the AI story's weakest links have been exposed, and if this were the tale about the Emperor's new clothes, the AI emperor is, if not naked, is having a wardrobe malfunction, for all to see. The Story Effect In this first week, as is to be expected, the response has been anything but reasoned. If you are a voracious reader of financial news (I am not), you have probably seen dozens of “thought pieces” from both technology and market experts claiming to foretell the future, and even among the few that I have read, the views range the spectrum on how DeepSeek changes the AI story. In my writings on narrative and numbers, where I talk about how every valuation tells a story, I also talk about how stories are dynamic, with a story break representing radical change (where a great story can crash and burn or a small story can break out to become a big one), a story change can be a significant narrative alteration (where a story adds or loses a dimension with big value effects) or a story shift (where the core story remains unchanged, but the parameters can change). Using the pre-DeepSeek story as a starting point, you can classify the narratives on what is coming on the story break/story change/story shift continuum: With all the caveats, including the fact that I am an AI novice, with a deeper understanding of potato chips than computer chips, and that it is early in the game, I am going to take a stand on where in this continuum I see the DeepSeek effect falling. I believe that DeepSeek does change the AI story, by creating two pathways to the AI product and service endgame. On one path that will lead to what I will term the “low intensity” AI market, it has opened the door to lower cost alternatives, in terms of investments in computing power and data, and competitors will flock in. That said, there will remain a segment of the AI market, where the old story will prevail, and the path of massive investments in computer chips and data centers leading to premium AI products and services will be the one that has to be taken. Note that the entry characteristics for the two paths will also determine the profitability and payoffs from their respective AI product and service markets (that will eventually exist). The “low entry cost” pathway is more likely to lead to commoditization, with lots of competitors and low pricing power, whereas the “high entry cost” path with its requirements for large upfront investment and access to data will create a more restrictive market, with higher priced and more profitable AI products and services. This story leaves me with a judgment call to make about the relative sizes of the markets for the two pathways. I am generalizing, but much of what consumers have seen so far as AI offerings fall into the low cost pathway and I would not be surprised, if that remains true for the most part. The DeepSeek entry has now made it more likely that you and I (as consumers) will see more AI products and services offered to us, at low cost or even for free. There is another segment of the AI products and services market, though, with businesses (or governments) as customers, where significant investments made and refinements will lead to AI products and services, with much higher price points. In this market, I would not be surprised to see networking benefits manifest, where the largest players acquire advantages, leading to winner-take-all markets. In telling this story, I understand that not only am I going to be wrong, perhaps decisively, but also that it could unravel in record time. I make this leap, not out of arrogance or a misplaced desire to change how you think, but because I own a slice of Nvidia (one quarter of the holding that I had two years ago, but still large enough to make a difference in my portfolio), and I cannot value the company without an AI story in place. That said, the feedback loop remains open, and I will listen not only to alternate opinions but also follow real world developments, in the interests of telling a better story. The Value Effect Now that my AI story is in the open, I will use it to revisit my valuation of Nvidia, and incorporate my new AI story in that valuation. Even without working through the numbers, it is very difficult to see a scenario where the entry of DeepSeek makes Nvidia a more valuable company, with the biggest change being in the expected size of the AI chip market: table.tableizer-table { font-size: 12px; border: 1px solid #CCC; font-family: Arial, Helvetica, sans-serif; } .tableizer-table td { padding: 4px; margin: 3px; border: 1px solid #CCC; } .tableizer-table th { background-color: #104E8B; color: #FFF; font-weight: bold; } In September 2024 (pre DeepSeek)In January 2025 (post DeepSeek) AI chip market size in 2035$500 billion$300 billion Nvidia's market share60%60% Nvidia's operating margin60%60% Nvidia's risk (cost of capital)10.52% _> 8.49%11.79% -> 8.50% (Higher riskfree rate + higher ERP) With the changes made, and updating the financials to reflect an additional quarter of data, you can see my Nvidia valuation in the picture below: Nvidia valuation in January 2025 (Post DeepSeek) There are two (unsurprising) results in this valuation. The value per share that I estimate for Nvidia dropped from $87 in September 2024 to $78 in January 2025, much of that change driven by the smaller AI chip market that comes out of the DeepSeek disruption (with the rest of the decline arising for higher riskfree rates and the equity risk premiums). The other is that the stock is overvalued, at its current price of $123 per share, even after the markdown this week. Since I found Nvidia overvalued in September 2024, when the big AI story was still in place, and Nvidia was trading at $109, $14 lower than todays price, estimating a lower value and comparing to a higher price makes it even more over valued.. More generally, the value effect of the DeepSeek disruption will be disparate, more negative for some companies in the AI space than others, and perhaps even positive for a few and I have attempted to capture those effects in the picture below, comparing DeepSeek to a bomb, and looking at the damage zones from the blast: In my view, the damage, in the near and long term, from DeepSeek will be to the businesses that have been the lead players in building the AI architecture. In addition to Nvidia (and its AI chip business), this includes the energy and gas businesses that have benefited from the tens of billions spent on building AI data centers. It is not that they will currently contracts, but that it is likely that you will see a slowing down of commitments to spend money on AI, as companies examine whether they need them. More companies are therefore likely to follow Apple's path of cautious entry than Meta and Microsoft's headfirst dive into the AI businesses. As for the businesses that are aiming for the AI products and services market, the effect will depend upon how much these products and services need data and computing power. If the proposed AI products and services are low-grade, i.e., they are more rule-based and mechanical and less dependent on incorporating intuition and human behavior, the effect of DeepSeek will be significant, with lower costs to entry and a commoditized marketplace, with lower margins and intense competition, If on the other hand, the AI products and services are high grade, i.e,, trying to imitate human decision making in the face of uncertainty, the effects of the DeepSeek entry are likely to be minimal and perhaps even non-existent. Thus, I would expect a business that is working on an AI product for financial accounting to find its business landscape changed more than Palantir, working on complex AI products for the defense department or commercial businesses. There is a grouping of companies, primarily big tech firms with large platforms, like Meta and Microsoft, where there may be buyer’s remorse about money already spent on AI (buying Nvidia chips and building data centers) but the DeepSea disruption may make it easier to develop low-cost, low-tech AI products and services that they can offer their platform users (either for free or at low costs) to keep them in their ecosystems. When faced with a development that could change the way we live and work, it is natural, especially in the early phases, to give that development a catchy name, and use it as a rationale for investing large amounts (if you are a business) or pushing up what you would pay for the businesses in the space (if you are an investor). In my early piece on AI, I talked about four developments in my lifetime that I would classify as revolutionary – personal computers in the 1980s, the internet in the 1990s, the smartphone in the first decade of the twenty first century and social media in the last decade, and how each of these started as catchall buzzwords, before investors and businesses learned to discriminate. Cisco, AOL and Amazon were all born in the internet era, but they had very different business models, and as the internet matured, faced very different end games. I hope that the DeepSeek entry into the AI narrative, and its disparate effects on different businesses in this space, will lead us to be more focused in our AI conversations. Thus, rather than describe a company as an AI company or describe the AI market as “huge”, we should be more explicit about what part of the AI business a company fits into (architecture, software, data or products/services) and apply the same degree of discrimination when talking about AI markets. If you also buy into my reasoning, you may want to follow up by asking whether the AI offering is more likely to fall into the premium or commoditized grouping. The Bottom Line My early entry into Nvidia and my holdings of many of the other Mag Seven stocks have allowed me to ride the AI boom, I have remained a skeptic about the product and service side of AI, for much of the last two years. I can attribute that wariness partly to my age, since I cannot think of a single AI offering that has been made to me in the last two years that I would pay a significant additional amount for. I see AI icons on almost everything that I use, from Zoom to Microsoft Word/Powerpoint/Excel to Apple mail. I must admit that they do neat things, including reword emails to not only clean up for mistakes but change the tone, but I can live without those neat add-ons. Since I work in valuation and corporate finance, not a day goes by without someone contacting me about a new AI product or service in the space. Having tried a few out, my response to many of these products and services is that, at least for me, they don’t do enough for me to bother. In many ways, DeepSeek confirms a long-standing suspicion on my part that most AI products and services that we will see, as consumers and even as businesses, fall into the “that’s cute” or “how neat” category, rather than into the “that would change my life”, If that is the case, it has also struck me as overkill to expend tens of billions of dollars building data centers to develop these products, akin to using a sledgehammer to tap a nail into the wall. Every major innovation of the last few decades, has had its reality check, and has emerged the stronger for it, and this may the first of many such reality checks for AI. I know that much of what I have said here goes against the "happy talk" narrative about AI, emanating from tech titans and business visionaries. I know that Reid Hoffman and Sam Altman believe that AI will be world-changing, in a good way, relieving us of the pain of tasks that are boring and time consuming, and even replacing flawed "human" decisions with be more reasoned AI decisions. They are smart men, but I have two reasons for being cautions. The first is that I have had exposure to smart people in almost every walk of life - smart academics, smart bankers, smart software engineers, smart venture capitalists and yes, even smart regulators - but most of them have had blind spots, perhaps because they hang out with people who think like them. The second, and this perhaps follows from the first, is that I am old enough to have heard this evangelist pitch for a revolutionary change before. In the 1980s, I remember being told that personal computers would eliminate the drudgery of working through ledger sheets with calculators and pencils, but as young financial analysts will tell you today, it has just created a fresh and perhaps even more soul-sucking drudgery, where monstrously large spreadsheets govern their workdays. In the 1990s, the advocates for the internet painted a picture of the world where access to online information would make us all more informed and wiser, but in hindsight, all it has done is weaken our reasoning muscles (by letting us look up answers online) and made us misinformed. In this century, social media too was born on the promise that it would keep us connected with friends, even if they were thousands of miles away, and happier, because of those connections, but as my good friend, Jonathan Haidt, and others have chronicled, it has left many in its orbit more isolated and less happy than before. YouTube Video Nvidia Valuations Nvidia valuation in September 2024 (Pre DeepSeek) Nvidia valuation in January 2025 (Post DeepSeek)
It was an interesting year for interest rates in the United States, one in which we got more evidence on the limited power that central banks have to alter the trajectory of market interest rates. We started 2024 with the consensus wisdom that rates would drop during the year, driven by expectations of rate cuts from the Fed. The Fed did keep its end of the bargain, cutting the Fed Funds rate three times during the course of 2024, but the bond markets did not stick with the script, and market interest rates rose during the course of the year. In this post, I will begin by looking at movements in treasury rates, across maturities, during 2024, and the resultant shifts in yield curves. I will follow up by examining changes in corporate bond rates, across the default ratings spectrum, trying to get a measure of how the price of risk in bond markets changed during 2024. Treasury Rates in 2024 Coming into 2024, interest rates had taken a rollicking ride, surging in 2022, as inflation made its come back, before settling in 2023. At the start of 2024, the ten-year treasury rate stood at 3.88%, unchanged from its level a year prior, but the 3-month treasury bill rate had climbed to 5.40%. In the chart below, we look the movement of treasury rates (across maturities) during the course of 2024: Download daily data During the course of 2024, long term treasury rates climbed in the first half of the year, and dropped in the third quarter, before reversing course and increasing in the fourth quarter, with the 10-year rate ending the year at 4.58%, 0.70% higher than at the start of the year. The 3-month treasury barely budged in the first half of 2024, declined in the third quarter, and diverged from long term rates and continued its decline in the last quarter, to end the year at 4.37%, down 1.03% from the start of the year. I have highlighted the three Fed rate actions, all cuts to the Fed Funds rate, on the chart, and while I will come back to this later in this post, market rates rose after all three. The divergence between short term and long term rates played out in the yield curve, which started 2024, with a downward slope, but flattened out over the course of the year: Download daily data Writing last year about the yield curve, which was then downward sloping, I argued that notwithstanding prognostications of doom, it was a poor prediction of recessions. This year, my caution would be to not read too much, at least in terms of forecasted economic growth, into the flattening or even mildly upward sloping yield curve. The increase in long term treasury rates during the course of the year was bad news for treasury bond investors, and the increase in the 10-year treasury bond rate during the course of the year translated into an annual return of -1.64% for 2024: With the inflation of 2.75% in 2024 factored in, the real return on the 10-year bond is -4.27%. With the 20-year and 30-year bonds, the losses become larger, as time value works its magic. It is one reason that I argue that any discussion of riskfree rates that does not mention a time horizon is devoid of a key element. Even assuming away default risk, a ten-year treasury is not risk free, with a one time horizon, and a 3-month treasury is definitely not riskfree, if you have a 10-year time horizon. The Drivers of Interest Rates Over the last two decades, for better or worse, we (as investors, consumers and even economics) seem to have come to accept as a truism the notion that central banks set interest rates. Thus, the answer to questions about past interest rate movements (the low rates between 2008 and 2021, the spike in rates in 2022) as well as to where interest rates will go in the future has been to look to central banking smoke signals and guidance. In this section, I will argue that the interest rates ultimately are driven by macro fundamentals, and that the power of central banks comes from preferential access to data about these fundamentals, their capacity to alter those fundamentals (in good and bad ways) and the credibility that they have to stay the course. Inflation, Real Growth and Intrinsic Riskfree Rates It is worth noting at the outset that interest rates on borrowing pre-date central banks (the Fed came into being in 1913, whereas bond markets trace their history back to the 1600s), and that lenders and borrowers set rates based upon fundamentals that relate specifically to what the former need to earn to cover expected inflation and default risk, while earning a rate of return for deferring current consumption (a real interest rate). If you set the abstractions aside, and remove default risk from consideration (because the borrower is default-free), a riskfree interest rate in nominal terms can be viewed, in its simplified form, as the sum of the expected inflation rate and an expected real interest rate: Nominal interest rate = Expected inflation + Expected real interest rate This equation, titled the Fisher Equation, is often part of an introductory economics class, and is often quickly forgotten as you get introduced to more complex (and seemingly powerful) monetary economics lessons. That is a pity, since so much of misunderstanding of interest rates stems from forgetting this equation. I use this equation to derive what I call an "intrinsic riskfree rate", with two simplifying assumptions: Expected inflation: I use the current year's inflation rate as a proxy for expected inflation. Clearly, this is simplistic, since you can have unusual events during a year that cause inflation in that year to spike. (In an alternate calculation, I use an average inflation rate over the last ten years as the expected inflation rate.) Expected real interest rate: In the last two decades, we have been able to observe a real interest rate, at least in the US, using inflation-protected treasury bonds(TIPs). Since I am trying to estimate an intrinsic real interest rate, I use the growth rate in real GDP as my proxy for the real interest rate. That is clearly a stretch when it comes to year-to-year movements, but in the long term, the two should converge. With those simplistic proxies in place, my intrinsic riskfree rate can be computed as follows: Intrinsic riskfree rate = Inflation rate in period t + Real GDP growth rate in period t In the chart below, I compare my estimates of the intrinsic riskfree rate to the observed ten-year treasury bond rate each year: Download data While the match is not perfect, the link between the two is undeniable, and the intrinsic riskfree rate calculations yield results that help counter the stories about how it is the Fed that kept rates low between 2008 and 2021, and caused them to spike in 2022. While it is true that the Fed became more active (in terms of bond buying, in their quantitative easing phase) in the bond market in the last decade, the low treasury rates between 2009 and 2020 were driven primarily by low inflation and anemic real growth. Put simply, with or without the Fed, rates would have been low during the period. In 2022, the rise in rates was almost entirely driven by rising inflation expectations, with the Fed racing to keep up with that market sentiment. In fact, since 2022, it is the market that seems to be leading the Fed, not the other way around. Entering 2025, the gap between intrinsic and treasury rates has narrowed, as the market consensus settles in on expectations that inflation will stay about the Fed-targeted 2% and that economic activity will be boosted by tax cuts and a business-friendly administration. The Fed Effect I am not suggesting that central banks don't matter or that they do not affect interest rates, because that would be an overreach, but the questions that I would like to address are about how much of an impact central banks have, and through what channels. To the first question of how much of an impact, I started by looking at the one rate that the Fed does control, the Fed Funds rate, an overnight interbank borrowing rate that nevertheless has resonance for the rest of the market. To get a measure of how the Fed Funds rate has evolved over time, take a look at what the rate has done between 1954 and 2024: As you can see the Fed Funds was effectively zero for a long stretch in the last decade, but has clearly spiked in the last two years. If the Fed sets rates story is right, changes in these rates should cause market set rates to change in the aftermath, and in the graph below, I look at monthly movements in the Fed Funds rate and two treasury rates - the 3-month T.Bill rate and the 10-year T.Bond rate. The good news for the "Fed did it" story is that the Fed rates and treasury rates clearly move in unison, but all this chart shows is that Fed Funds rate move with treasury rates contemporaneously, with no clear indication of whether market rates lead to Fed Funds rates changing, or vice versa. To look at whether the Fed funds leads the rest of the market, I look at the correlation between changes in the Fed Funds rate and changes in treasury rates in subsequent months. As you can see from this table, the effects of changes in the Fed Funds rate on short term treasuries is positive, and statistically significant, but the relationship between the Fed Funds rate and 10-year treasuries is only 0.08, and barely meets the statistical significance test. In summary, if there is a case to be made that Fed actions move rates, it is far stronger at the short end of the treasury spectrum than at the long end, and with substantial noise in predictive effects. Just as an add on, I reversed the process and looked to see if the change in treasury rates is a good predictor of change in the Fed Funds rate and obtained correlations that look very similar. In short, the evidence is just as strong for the hypothesis that market interest rates lead the Fed to act, as they are for "Fed as a leader" hypothesis. As to why the Fed's actions affect market interest rates, it has less to do with the level of the Fed Funds rate and more to do with the market reads into the Fed's actions. Ultimately, a central bank's effect on market interest rates stems from three factors: Information: It is true that the Fed collects substantial data on consumer and business behavior that it can use to make more reasoned judgments about where inflation and real growth are headed than the rest of the market, and its actions often are viewed as a signal of that information. Thus, an unexpected increase in the Fed Funds rate may signal that the Fed sees higher inflation than the market perceives at the moment, and a big drop in the Fed Funds rates may indicate that it sees the economy weakening at a time when the market may be unaware. Central bank credibility: Implicit in the signaling argument is the belief that the central bank is serious in its intent to keep inflation in check, and that is has enough independence from the government to be able to act accordingly. A central bank that is viewed as a tool for the government will very quickly lose its capacity to affect interest rates, since the market will tend to assume other motives (than fighting inflation) for rate cuts or raises. In fact, a central bank that lowers rates, in the face of high and rising inflation, because it is the politically expedient thing to do may find that market interest move up in response, rather than down. Interest rate level: If the primary mechanism for central banks signaling intent remains the Fed Funds rate (or its equivalent in other markets), with rate rises indicating that the economy/inflation is overheating and rate cuts suggesting the opposite, there is an inherent problem that central banks face, if interest rates fall towards zero. The signaling becomes one sided i.e., rates can be raised to put the economy in check, but there is not much room to cut rates. This, of course, is exactly what the Japanese central bank has faced for three decades, and European and US banks in the last decade, reducing their signal power. The most credible central banks in history, from the Bundesbank in Deutsche Mark Germany to the Fed, after the Volcker years, earned their credibility by sticking with their choices, even in the face of economic disruption and political pushback. That said, in both these instances, central bankers chose to stay in the background, and let their actions speak for themselves. Since 2008, central bankers, perhaps egged on by investors and governments, have become more visible, more active and, in my view, more arrogant, and that, in a strange way, has made their actions less consequential. Put simply, the more the investing world revolves around FOMC meetings and the smoke signals that come out of them, the less these meetings matter to markets. Forecasting Rates I am wary of Fed watchers and interest rate savants, who claim to be able to sense movements in rates before they happen for two reasons. First, their track records are so awful that they make soothsayers and tarot card readers look good. Second, unlike a company's earnings or risk, where you can claim to have a differential advantage in estimating it, it is unclear to me what any expert, no matter how credentialed, can bring to the table that gives them an edge in forecasting interest rates. In my valuations, this skepticism about interest rate forecasting plays out in an assumption where I do not try to second guess the bond market and replace current treasury bond rates with fanciful estimates of normalized or forecasted rates. If you look back at my S&P 500 valuation in my second data post for this year, you will see that I left the treasury bond rate at 4.58% (its level at the start of 2025) unchanged through time. If you feel the urge to play interest forecaster, I do think that it is good practice to make sure that your views on the direction of interest rates are are consistent with the views of inflation and growth you are building into your cash flows. If you buy into my thesis that it is changes in expected inflation and real growth that causes rates to change in interest rates, any forecast of interest rates has be backed up by a story about changing inflation or real growth. Thus, if you forecast that the ten-year treasury rate will rise to 6% over the next two years, you have to follow through and explain whether rising inflation or higher real growth (or both) that is triggering this surge, since that diagnosis have different consequences for value. Higher interest rates driven by higher inflation will generally have neutral effects on value, for companies with pricing power, and negative effects for companies that do not. Higher interest rates precipitated by stronger real growth is more likely to be neutral for the market, since higher earnings (from the stronger economy) can offset the higher rates. The most empty forecasts of interest rates are the ones where the forecaster's only reason for predicting higher or lower rates is central banks, and I am afraid that the discussion of interest rates has become vacuous over the last two decades, as the delusion that the Fed sets interest rates becomes deeply engrained. Corporate Bond Rates in 2024 The corporate bond market gets less attention that the treasury bond market, partly because rates in that market are very much driven by what happens in the treasury market. Last year, as the treasury bond rate rose from 3.88% to 4.58%, it should come as no surprise that corporate bond rates rose as well, but there is information in the rate differences between the two markets. That rate difference, of course, is the default spread, and it will vary across different corporate bonds, based almost entirely on perceived default risk. Default spread = Corporate bond rate - Treasury bond rate on bond of equal maturity Using bond ratings as measures of default risk, and computing the default spreads for each ratings class, I captured the journey of default spreads during 2024: During 2024, default spreads decreased over the course of the year, for all ratings classes, albeit more for the lowest rated bonds. Using a different lexicon, the price of risk in the bond market decreased during the course of the year, and if you relate that back to my second data update, where I computed a price of risk for equity markets (the equity risk premium), you can see the parallels. In fact, in the graph below, I compare the price of risk in both the equity and bond markets across time: In most years, equity risk premiums and bond default spreads move in the same direction, as was the case in 2024. That should come as little surprise, since the forces that cause investors to spike up premiums (fear) or bid them down (hope and greed) cut across both markets. In fact, lookin a the ratio of the equity risk premium to the default spread, you could argue that equity risk premiums are too high, relative to bond default spreads, and that you should see a narrowing of the difference, either with a lower equity premium (higher stock prices) or a higher default spread on bonds. The decline of fear in corporate bond markets can be captured on another dimension as well, which is in bond issuances, especially by companies that face high default risk. In the graph below, I look at corporate bond issuance in 2024, broken down into investment grade (BBB or higher) and high yield (less than BBB). Note that high yield issuances which spiked in 2020 and 2021, peak greed years, almost disappeared in 2022. They made a mild comeback in 2023 and that recovery continued in 2024. Finally, as companies adjust to a new interest rate environment, where short terms rates are no longer close to zero and long term rates have moved up significantly from the lows they hit before 2022, there are two other big shifts that have occurred, and the table below captures those shifts: First, you will note that after a long stretch, where the percent of bond that were callable declined, they have spiked again. That should come as no surprise, since the option, for a company, to call back a bond is most valuable, when you believe that there is a healthy chance that rates will go down in the future. When corporates could borrow money at 3%, long term, they clearly attached a lower likelihood to a rate decline, but as rates have risen, companies are rediscovering the value of having a calculability option. Second, the percent of bond issuances with floating rate debt has also surged over the last three years, again indicating that when rates are low, companies were inclined to lock them in for the long term with fixed rate issuances, but at the higher rates of today, they are more willing to let those rates float, hoping for lower rates in future years. In Conclusion I spend much of my time in the equity market, valuing companies and assessing risk. I must confess that I find the bond market far less interesting, since so much of the focus is on the downside, and while I am glad that there are other people who care about that, I prefer to operate in a space where there there is more uncertainty. That said, though, I dabble in bond markets because what happens in those markets, unlike what happens in Las Vegas, does not stay in bond markets. The spillover effects into equity markets can be substantial, and in some cases, devastating. In my posts looking back at 2022, I noted how a record bad year for bond markets, as both treasury and corporate bonds took a beating for the ages, very quickly found its ways into stocks, dragging the market down. On that count, bond markets had a quiet year in 2024, but they may be overdue for a clean up. YouTube Data Updates for 2025 Data Update 1 for 2025: The Draw (and Danger) of Data! Data Update 2 for 2025: The Party continued for US Equities Data Update 3 for 2025: The times they are a'changin'! Data Update 4 for 2025: Interest Rates, Inflation and Central Banks! Data Links Intrinsic risk free rates and Nominal interest rates Bond Default Spreads and Equity Risk Premiums
In my first two data posts for 2025, I looked at the strong year that US equities had in 2024, but a very good year for the overall market does not always translate into equivalent returns across segments of the market. In this post, I will remain focused on US equities, but I will break them into groupings, looking for differences. I first classify US stocks by sector, to see return variations across different industry groupings. I follow up by looking at companies broken down by market capitalization, with an eye on whether the much-vaunted small cap premium has made a comeback. In the process, I also look how much the market owes its winnings to its biggest companies, with the Mag Seven coming under the microscope. In the next section, I look at stock returns for companies in different price to book deciles, in a simplistic assessment of the value premium. With both the size and value premiums, I will extend my assessment over time to see how (and why) these premiums have changed, with lessons for analysts and investors. In the final section, I look at companies categorized by price momentum coming into 2024, to track whether winning stocks in 2023 were more likely to be winners or losers in 2024. US Stocks, by Sector (and Industry) It is true that you very seldom see a market advance that is balanced across sectors and industries. This market (US stocks in 2024) spread its winnings across sectors disproportionately, with four sectors - technology, communication services, consumer discretionary and financials - delivering returns in excess of 20% in 2024, and three sectors - health care, materials and real estate delivering returns close to zero: Sector Returns - Historical (with $ changes in millions) The performance of technology stocks collectively becomes even more impressive, when you look at the fact that they added almost $4.63 trillion in market cap just in 2024, and that over the last five (ten) years, the sector has added $11.3 trillion ($13.6 trillion) in market cap. I break the sectors down into 93 industries, to get a finer layer of detail, and there again there are vast differences between winning and losing industry groups, based upon stock price performance in 2024: $ changes in millions While most of the industries on the worst-performing list represent old economy companies (steel, chemicals, rubber & tires), green energy finds itself on the list as well, perhaps because the "virtue trade" (where impact and socially conscious investors bought these companies for their greenness, rather than business models) lost its heft. The top two performers, in 2024, on the best performing industry list, semiconductors and auto & truck, owe much of their overall performance to super-performers in each one (Nvidia with semiconductors and Tesla with auto & truck), but airline companies also had a good year, though it may be premature to conclude that they have finally found working business models that can deliver profitability on a continuous basis. US Stocks, by Market Cap For much of the last century, the conventional wisdom has been that small companies, with size measured by market cap, deliver higher returns than larger companies, on a risk-adjusted basis, with the debate being about whether that was because the risk measures were flawed or because small cap stocks were superior investments. That "small cap premium" has found its way into valuation practitioners playbooks, manifesting as an augmentation (of between 3-5%) on the cost of equity of small companies. To get a sense of how market capitalization was related to returns, I classified all publicly traded US companies, by market cap, and looked at their returns in 2024. The returns across deciles are volatile, and while the lowest deciles in terms of market cap deliver higher percent returns, looking at the top and bottom halves of the market, in terms of market cap, you can see that there is not much setting apart the two groups. To make an assessment of how the performance of small cap stocks in 2024 falls in the historical spectrum, I drew on Ken French's research return data, one of my favorite data sources, and looked at the small cap premium as the difference in compounded annual returns between the lowest and highest deciles of companies, in terms of market cap: My small cap premium spreadsheet, based on Ken French data In this graph, you can see the basis for the small cap premium, but only if go back all the way to 1927, and even with that extended time period, it is far stronger with equally weighted than with value weighted returns; the 1927-2024 small cap premium is 2.07% with value-weighted returns and 6.69% in equally weighted terms. It should be noted that even its heyday, the small cap premium had some disconcerting features including the facts that almost of it was earned in one month (January) of each year, and that it was sensitive to starting and end points for annual data, with smaller premium in mid-year starting points. To see how dependent this premium is on the front end of the time period, I estimated the small cap premium with different starting years in the graph (and the table), and as you can see the small cap premium drops to zero with any time period that starts in 1970 and beyond. In fact, the small cap premium has become a large cap premium for much of this century, with small cap returns lagging large cap returns by about 4-4.5% in the last 20 years. The market skew towards large cap companies can be seen even more dramatically, if you break stocks down by percentile, based upon market cap, and look at how much of the increase in market cap in US equities is accounted for by different percentile groupings: US Stocks: Market Cap Change Breakdown Looking across 6000 publicly traded stocks in 2024, the top percentile (about 60 stocks) accounted for 74% of the increase in market cap, and the top ten percent of all stocks delivered 94% of the change in total market capitalization. Zeroing in even further and looking at the biggest companies in the top percentile, the Mag Seven, the concentration of winners at the very top is clear: $ changes in millions In 2024, seven companies (Apple, Amazon, Meta, Alphabet, Microsoft, Nvidia and Tesla) increased in market cap by $5.6 trillion, almost of the entire market's gain for the year. While it is not uncommon for stock market returns to be delivered by a few winners at the top, with the Mag Seven, the domination extends over a decade, and in the last ten years (2014-2024), these seven companies have added $15.8 trillion in market cap, about 40% of the increase in market capitalization across all US stocks over the decade. For years now, some investors have bet on a reversal in this trend line, with small cap stocks coming back in favor, and these investors have lagged the market badly. To get a better handle on why large cap stocks have acquired a dominant role, in markets, I look at three explanations that I have seen offered for the phenomenon: Momentum story: Momentum has always been a strong force in markets, in both directions, with price increases in stocks (decreases) followed by more price increases (decreases). In effect, winning stocks continue to win, drawing in new funds and investors, but when these same stocks start losing, the same process plays out in reverse. A reasonable argument can be made that increasing access to information and easing trading, for both individual and institutional investing, with a boost from social media, has increased momentum, and thus the stock prices of large cap stocks. The dark side of this story, though, is that if the momentum ever shifted, these large cap stocks could lose trillions in value. Passive investing: Over the last two decades, passive investing (in the form of index funds and ETFs) has taken market share from active investors, accounting for close to 50% of all invested funds in 2024. That shift has been driven by active investing underperformance and a surge in passive investing vehicles that are accessible to all investors. Since many passive investing vehicles hold all of the stocks in the index in proportion to their market cap, there presence and growth creates fund flows into large cap stocks and keeps their prices elevated. Here again, the dark side is that if fund flows reverse and became negative, i.e., investors start pulling money out of markets, large cap stocks will be disproportionately hurt. Industry economics: In writing about the disruption unleashed by tech start-ups, especially in the last two decades, I have noted the these disruptors have changed industry economics in many established businesses, replacing splintered, dispersed competition with consolidation. Thus, Meta and Alphabet now have dominant market shares of the advertising business, just as Uber, Lyft and Grab have consolidated the car service business. As industries consolidate, we are likely to see them dominated by a few, big winners, which will play out in the stock market as well. It is possible that antitrust laws and regulatory authorities will try to put constraints on these biggest winners, but as I noted in my post on the topic, it will not be easy. In my view, the small cap premium is not coming back, and given that it has been invisible for five decades now, the only explanation for why appraisers and analysts hold on to it is inertia. That said, the large cap premium that we have seen in the last two decades, was businesses have transitioned from splintered to consolidated structure, will also fade. Where does that leave us? Picking a company to invest in, based upon its market capitalization, will be, at best, a neutral strategy, and that should surprise no one. The Value Premium? Just as the small cap premium acquired standing as conventional wisdom in the twentieth century, the data and research also indicated that stocks that trade at low price to book ratios earned higher returns that stocks that trade at high price to book ratios, in what was labeled as the value premium. As with the size premium, low price to book (value) stocks have struggled to deliver in the twenty first century, and as with the small size premium, investors have waited for it to return. To see how stocks in different price to book classes performed in 2024, I looked at returns in 2024, for all US stocks, broken down into price to book deciles: Deciles created based on price to book ratios at start of 2024 In 2024, at least, it was the companies in the top decile (highest price to book ratios) that delivered the best returns in 2024, and stocks in the lowest decile lagged the market. Here again, Ken French's data is indispensable in gaining historical perspective, as I looked the difference in annual returns between the top decile and bottom decile of stocks, classified by price to book, going back to 1927: My value premium spreadsheet, based on Ken French data In this graph, I am computing the premium earned by low price to book stocks, in the US, with different starting points. Thus, if you go back to 1927 and look at returns on the lowest and highest deciles, the lowest decile earned an annual premium of 2.43%. That premium remains positive until you get to about 1990, when it switches signs; the lowest price to book stocks have earned 0.87% less annually between 1990 and 2024, than the highest price to book stocks. As was the case with the small cap premium, the premium earned by low price to book stocks over high price to book stocks has faded over time, spending more time in negative territory in the last 20 years, than positive. Value investors, or at least the ones that use the conventional proxies for cheapness (low price to book or low PE ratios), have felt the effects, significantly under performing the market for much of the last two decades. While some of them still hold on to the hope that this is just a phase that will reverse, there are three fundamentals at play that may indicate that the low price to book premium will not be back, at least on a sustained basis: Price to book ≠ Value: It is true that using low price to book as an indicator of value is simplistic, and that there are multiple other factors (good management, earnings quality, moats) to consider before making a value judgment. It is also true that as the market's center of gravity has shifted towards companies with intangible assets, the troubles that accountants have had in putting a number on intangible asset investments has made book value less and less meaningful at companies, making it a poorer and poorer indicator of what a company's assets are worth. Momentum: In markets, the returns to value investing has generally moved inversely with the strength of momentum. Thus, the same forces that have strengthened the power of momentum, that we noted in the context of the fading of the small cap premium, have diluted the power of value investing. Structural Shifts: At the heart of the premium earned by low price to book ratios is mean reversion, with much of the high returns earned by these stocks coming from moving towards the average (price to book) over time. While that worked in the twentieth century, when the US was the most mean-reverting and predictable market/economy of all time, it has lost its power as disruption and globalization have weakened mean reversion. So, what does this mean for the future? I see no payoff in investing in low price to book stocks and waiting for the value premium to return. As with market cap, I believe that the value effect will become volatile, with low price to book stocks winning in some years and high price to book stocks in others, and investing in one or another of these groups, just on the basis of their price to book ratios, will no longer deliver excess returns. Since the fading of the small cap and value premiums can be traced at least partially to the strengthening of momentum, as a market force, I looked at the interplay between momentum and stock returns, by breaking companies into deciles, based upon stock price performance in the previous year (2023), and looking at returns in 2024: Deciles formed on percentage returns in 2023 As you can see, barring the bottom decile, which includes the biggest losers of 2023, where there was a strong bounce back (albeit less in dollar terms, than in percent), there was a strong momentum effect in 2024, with the biggest winners from last year (2023) continuing to win in 2024. In short, momentum continued its dominance in 2024, good news for traders who make money in its tailwinds, with the caveat that momentum is a fickle force, and that 2025 may be the year where it reverses. Implications The US equity market in 2024 followed a pathway that has become familiar to investor in the last decade, with large companies, many with a tech focus, carried the market, and traditional strategies that delivered higher returns, such as investing in small cap or low price to book stocks, faltered. This is not a passing phase, and reflects the market coming to terms with a changed economic order and investor behavior. There are lessons from the year for almost everyone in the process, from investors to traders to corporate executive and regulators: For investors: I have said some harsh things about active investing, as practiced today, since much of it is based upon history and mean reversion. A mutual fund manager who screens stocks for low PE ratios and high growth, while demanding a hefty management fee, deserves to be replaced by an ETF or index fund, and that displacement will continue, pruning the active management population. For active investors who hold on to the hope that quant strategies or AI will let them rediscover their mojo, I am afraid that disappointment is awaiting them. For traders: Traders live and die on momentum, and as market momentum continues to get stronger, making money will look easy, until momentum shifts. Coming off a year like 2024, where chasing momentum would have delivered market-beating returns, the market may be setting up traders for a takedown. It may be time for traders to revisit and refine their skills at detecting market momentum shifts. For companies: Companies that measure their success through stock market returns may find that the market price has become a noisier judge of their actions. Thus, a company that takes a value destructive path that feeds into momentum may find the market rewarding it with a higher price, but it is playing a dangerous game that could turn against it. For regulators: With momentum comes volatility and corrections, as momentum shifts, and those corrections will cause many to lose money, and for some, perhaps even their life savings. Regulators will feel the pressure to step in and protect these investors from their own mistakes, but in my view, it will be futile. In the markets that we inhabit, literally any investment can be an instrument for speculation. After all, Gamestop and AMC were fairly stolid stocks until they attracted the meme crowd, and Microstrategy, once a technology firm, has become almost entirely a Bitcoin play. I recently watched Timothy Chalamet play Bob Dylan in the movie, A Complete Unknown, and I was reminded of one of my favorite Dylan tunes, "The times they are a-changin". I started my investing in the 1980s, in a very different market and time, and while I have not changed my investing principles, I have had to modify and adapt them to reflect a changed market environment. You may not agree with my view that both the small cap and value premiums are in our past, but it behooves you to question their existence. YouTube Video Data Updates for 2025 Data Update 1 for 2025: The Draw (and Danger) of Data! Data Update 2 for 2025: The Party continued for US Equities Data Update 3 for 2025: The times they are a'changin'! Data Update 4 for 2025: Interest Rates, Inflation and Central Banks! Datasets My small cap premium calculator (based on Ken French data) My value premium calculator (based on Ken French data)
In my last post, I noted that the US has extended its dominance of global equities in recent years, increasing its share of market capitalization from 42% in at the start of 2023 to 44% at the start of 2024 to 49% at the start of 2025. That rise was driven by a surge in US equity values during 2024, with the S&P 500 delivering returns of close to 25%, all the more impressive, given that the index delivered returns in excess of 26% in 2023. In this post, I will zero in on US equities, in the aggregate, first by looking at month-by-month returns during 2024, and then putting their performance in the last two years in a historical context. I will follow up by trying to judge where markets stand at the start of 2025, starting with PE ratios, moving on to earnings yields and ending with a valuation of the index. US Equities in 2024 Entering 2024, there was trepidation about where stocks would go during the year especially coming off a a strong bounce back year in 2023, and there remained real concerns about inflation and a recession. The hopeful note was that the Fed would lower the Fed Funds rate during the course of the year, triggering (at least in the minds of Fed watchers) lower interest rates across the yield curve, Clearly, the market not only fought through those concerns, but did so in the face of rising treasury rates, especially at the long end of the spectrum. While the market was up strongly for the year, it is worth remembering that the there were months during 2024, where the market looked shaky, as can be seen in the month to month returns on the S&P 500 during the course of 2024: The market’s weakest month was April 2024, and it ended the year or a weak note, down 2.50% in December. Overall, though the index was up 23.31% for the year, and adding the dividend yield of 1.57% (based upon the expected dividends for 2025 and the index at the start of the years) yields a total return 24.88% for the year: As is almost always the case, the bulk of the returns from equity came from price appreciation, with the caveat that the dividend yield portion has shrunk over the last few decades in the United States. Historical Context To assess stock returns in 2024, it makes sense to step back and put the year's performance into historical perspective. In the graph below, I look at returns (inclusive of dividends) on the S&P 500 every year from 1928 to 2024. Download historical data Across the 97 years that I have estimated annual returns, stocks have had their ups and downs, delivering positive returns in 71 years and negative returns in the other 26 years. The worst year in history was 1931, with stocks returning -43.84%, and the best year was 1954, when the annual return was 52.56%. If you wanted to pick a benchmark to compare annual returns to pass judgment on whether a year was above or below average, you can can go with either the annual return (11.79%) or the median return (14.82%) across the entire time period. Looking at the 24.88% return in 2024 in terms of rankings, it ranks as the 27th best year across the last 97 years, indicating that while it was a good year, there have been far better years for US stocks. Combining 2023 and 2024 returns yield a cumulative a two-year return for the S&P 500 of 57.42%, making it one the ten best two-year periods in US market history. The riskless alternative to investing in US stocks during this period, in US dollar terms, are US treasuries, and in 2024, that contest was won, hands down, by US equities: Equity risk premium earned in 2024, over 3-month treasury bills = Return on stocks - Return on 3-month treasuries (averaged over 2024) = 24.88% -4.97% = 19.91% Equity risk premium earned in 2024, over 10-year treasuries = Return on stocks - Return on 10-year treasury = 24.88% -(-1.64%) = 26.52% The ten-year treasury return was negative, because treasury bond rates rose during 2024. Equity risk premiums are volatile over time, and averaging them makes sense, and in the table below, I look at the premium that stocks have earned over treasury bills and treasury bonds, going back to 1928, using both simple averages (of the returns each year) and geometric averages (reflecting the compounding effect): Download historical data These returns are nominal returns, and inflation would have taken a bite out of returns each year. Computing the returns in real terms, by taking out inflation in each year from that year's returns, and recomputing the equity risk premiums: Download historical data Note that the equity risk premiums move only slightly, because inflation finds its way into both stock and treasury returns. Many valuation practitioners use these historical averages, when forecasting equity risk premiums in the future, but it is a practice that deserves scrutiny, partly because it is backward looking (with the expectation that things will revert back to the way they used to be), but mostly because the estimates that you get for the equity risk premium have significant error terms (see standard errors listed below the estimates in the table). Thus, if are using the average equity risk premium for the last 97 years of 5.44% (7.00%), i.e., the arithmetic or geometric averages, it behooves you to also inform users that the standard error of 2.12% will create a range of about 4% on either side of the estimate. Pricing Questions Coming into 2025, investors are right to be trepidatious, for many reasons, but mostly because we are coming off two extraordinarily good years for the market, and a correction seems due. That is, however, a poor basis for market timing, because stock market history is full of examples to the contrary. There are other metrics, though, which are signaling danger, and in this section, I will wrestle with what they tell us about stocks in 2025. PE ratios and Earnings Yields Even as we get new and updated pricing metrics, it is undeniable that the most widely used metric of stock market cheapness or expensiveness is the price earnings ratio, albeit with variations in the earning number that goes into the denominator on timing (current, last 12 months or trailing or next 12 month of forward), share count (diluted, primary) and measurement (ordinary or extraordinary). In the graph below, I focus on trailing earnings for all companies in the S&P 500 and compute the aggregated PE ratio for the index to be 24.16 at the start of 2025, higher than the average value for that ratio in every decade going back to 1970. Download data Just for completeness, I compute two other variants of the PE, the first using average earnings over the previous ten years (normalized) and the second using the average earnings over the last ten years, adjusted for inflation (CAPE or Shiller PE). At the start of 2025, the normalized PE and CAPE also come in at well above historical norms. If I have terrified you with the PE story, and you have undoubtedly heard variants of this story from market experts and strategists for much of the last decade, I would hasten to add that investing on that basis would have kept you out of stocks for much of the last ten years, with catastrophic consequences for your portfolio. For some of this period, at least, you could justify the higher PE ratios with much lower treasury rates than historic norms,, and one way to see this is to compare the earnings yield, i.e., the inverse of the PE ratio, with the treasury yields, which is what I have done in the graph below: Download data If you compare the earnings yield to the ten-year treasury rate, you can see that for much of the last decade, going into 2022, the earnings yield, while low, was in excess of the ten-year rate. As rates have risen, though, the difference has narrowed, and at the start of 2025, the treasury rate exceeded the earnings yield. If you see market strategists or journalists talking about negative equity risk premiums, this (the difference between the earnings yield and the treasury rate) is the number that they are referencing. At this stage, you may be ready to bail on stocks, but I have one final card to play. In a post in 2023, I talked about equity risk premiums, and the implicit assumptions that you make when you use the earning to price ratio as your measure of the expected return on stocks. It works only if you make one of two assumptions: That there will be no growth in earnings in the future, i.e., you will earn last year's earnings every year in perpetuity, making stocks into glorified bonds. In a more subtle variants, there will be growth, but that growth will come from investments that earn returns equal to the cost of equity. The problem with both assumptions is that they are in conflict with the data. First, the earnings on the S&P 500 companies has increased 6.58% a year between 2000 and 2024, making the no-growth assumption a non-started. Second, the return on equity for the S&P 500 companies was 20.61% in 2023, and has averaged 16.38% since 2000, both numbers well in excess of the cost of equity. So, what is the alternative? Starting 30 years ago, I began estimating a more complete expected return on stocks, using the S&P 500, with the level of the index standing in for the price you pay for stocks, and expected earnings and cash flows, based upon consensus estimates of earnings and cash payout ratios. I solve for an internal rate of return for stocks, based upon these expected cash flows: The expected return from this approach will be different from the earnings to price ratio because it incorporate expected growth and changes in cash flow patterns. The critique that this approach requires assumptions about the future (growth and cash flows) is disingenuous, since the earnings yield approach makes assumptions about both as well (no growth or no excess returns), and I will wager that the full ERP approach is on more defensible ground than the earning yield approach. Using this approach at the start of 2025 to the S&P 500, I back out an implied expect return of 8.91% for the index, and an implied equity risk premium of 4.33% (obtained by netting out the ten-year bond rate on Jan 1, 2025, of 4.58%): Implied ERP calculation in 2025 You are welcome to take issue with the number that I use there, lowering the growth rates for the future or changing the assumptions about payout. That is a healthy debate, and one that provides far more room for nuance that looking at the earnings yield. How does an implied equity risk premium play out in market level arguments? Every argument about markets (from them being in a bubble to basement level bargains) can be restated in terms of the equity risk premium. If you believe that the equity risk premium today (4.33%) is too low, you are, in effect, stating that stocks are overvalued, and if you view it as too high, you are taking the opposite position. If you are not in the market timing business, you take the current premium as a fair premium, and move on. To provide perspective on the ERP at the start of 2025, take a look at this graph, that lists implied ERP at the start of each year going back to 1960: Historical implied ERP There is something here for almost point of view. If you are sanguine about stock market levels, you could point to the current premium (4.33%) being close to the historical average across the entire time period (4.25%). If you believe that stocks are over priced, you may base that on the current premium being lower than the average since 2005. I will not hide behind the "one hand, other hand" dance that so many strategists do. I think that we face significant volatility (inflation, tariffs, war) in the year to come, and I would be more comfortable with a higher ERP. At the same time, I don't fall into the bubble crowd, since the ERP is not 2%, as it was at the end of 1999. Valuation Questions Pulling together the disparate strands that are part of this post, I valued the index at the start of 2025, using the earnings expectations from analysts as the forecasted earnings for 2025 and 2026, before lowering growth rates to match the risk free rate in 2029. As the growth rates changes, I also adjust the payout ratios, given the return on equity for the S&P 500 companies: Download spreadsheet With the assumption that the equity risk premium will climb back to 4.5%, higher than the average for the 1960-2024 period, but lower than the post-2008 average, the value that I get for the index is about 5260, about 12% lower than the index at the start of the year. Note that this is a value for the index today, and if you wanted to adopt the market strategist approach of forecasting where the index will be a year from now, you would have to grow the value at the price appreciation portion (about 7.5%) of the expected return (which is 9.08%). As I see it, there are two major dangers that lurk, with the first being higher inflation (translating into higher treasury rates) and the second being a market crisis that will push up the equity risk premium, since with those pieces in play, the index becomes much more significantly over valued. From an earnings perspective, the risk is that future earnings will come in well below expectations, either because the economy slows or because of trade frictions. Rather than wring my hands about these uncertainties, I fell back on a tool that I use when confronted with change, which is a simulation: Crystal Ball used for simulations While the base case conclusion that the market is overvalued stays intact, not surprising since my distributions for the input variables were centered on my base assumptions, there is a far richer set of output. Put simply, at today's price levels, there is an 80% chance that stocks are overvalued and only a 20% chance that they are undervalued. That said, though, if you are bullish, I can see a pathway to getting to a higher value, with higher earnings, lower interest rates and a continued decline in the equity risk premium. Conversely, you are bearish, I understand your point of view, especially if you see earnings shocks (from a recession or a tariff war), rising inflation or a market crisis coming up. I don't dish out market advice, and as one whose market timing skills are questionable, you should not take my (or anyone else's) assessments at face value, especially heading into a year, where change will be the byword. It is possible that lower taxes and less regulation may cause to come in higher than expected, and that global investment fund flows will keep interest rates and equity risk premiums low. My advice is that you download the valuation spreadsheet, change the inputs to reflect your views of the world, and value the index yourself. Good investing requires taking ownership of the decisions and judgments you make, and I am glad to provide tools that help you in that process. YouTube Video Data Updates for 2025 Data Update 1 for 2025: The Draw (and Danger) of Data! Data Update 2 for 2025: The Party continued for US Equities Data Update 3 for 2025: The times they are a'changin'! Data Update 4 for 2025: Interest Rates, Inflation and Central Banks! Datasets Historical returns on stocks: https://pages.stern.nyu.edu/~adamodar/pc/datasets/histretSP.xlsx Historical implied ERP: https://pages.stern.nyu.edu/~adamodar/pc/datasets/histimpl.xls PE ratios for the S&P 500: https://pages.stern.nyu.edu/~adamodar/pc/PEforS&P500updatedJan25.xlsx Spreadsheets Implied ERP at the start of 2025: https://pages.stern.nyu.edu/~adamodar/pc/implprem/ERPJan25.xlsx Valuation of the index on Jan 1, 2025: https://pages.stern.nyu.edu/~adamodar/pc/blog/S&PValueJan2025.xlsx
For the last four decades, I have spent the first week of each year collecting and analyzing data on publicly traded companies and sharing what I find with anyone who is interested. It is the end of the first full week in 2025, and my data update for the year is now up and running, and I plan to use this post to describe my data sample, my processes for computing industry statistics and the links to finding them. I will also repeat the caveats about how and where the data is best used, that I have always added to my updates. The Draw (and Dangers) of Data It is the age of data, as both companies and investors claim to have tamed it to serve their commercial interests. While I believe that data can lead to better decisions, I am wary about the claims made about what it can and cannot do in terms of optimizing decision making. I find its greatest use is on two dimensions: Fact-checking assertions: It has always been true that human beings assert beliefs as facts, but with social media at play, they can now make these assertion to much bigger audiences. In corporate finance and investing, which are areas that I work in, I find myself doing double takes as I listen to politicians, market experts and economists making statements about company and market behavior that are fairy tales, and data is often my weapon for discerning the truth. Noise in predictions: One reason that the expert class is increasingly mistrusted is because of the unwillingness on the part of many in this class to admit to uncertainty in their forecasts for the future. Hiding behind their academic or professional credentials, they ask people to trust them to be right, but that trust has eroded. If these predictions are based upon data, as they claim they are, it is almost always the case that they come with error (noise) and that admitting to this is not a sign of weakness. In some cases, it is true that the size of that errors may be so large that those listening to the predictions may not act on them, but that is a healthy response. As I listen to many fall under the spell of data, with AI and analytics add to its allure, I am uncomfortable with the notion that data has all of the answers, and there two reasons why: Data can be biased: There is a widely held belief that data is objective, at least if it takes numerical form. In the hands of analysts who are biased or have agendas, data can be molded to fit pre-conceptions. I would like to claim to have no bias, but that would be a lie, since biases are often engrained and unconscious, but I have tried, as best as I can, to be transparent about the sample that I use, the data that I work with and how I compute my statistics. In some cases, that may frustrate you, if you are looking for precision, since I offer a range of values, based upon different sampling and estimation choices. Taking a look at my tax rate calculations, by industry, for US companies, int the start of 2025, I report the following tax rates across companies. Effective tax rates, by Industry (US) Note, that the tax rates for US companies range from 6.75% to 26.43%, depending on how I compute the rate, and which companies I use to arrive at that estimate. If you start with the pre-conception that US companies do not pay their fair share in taxes, you will latch on to the 6.75% as your estimated tax rate, whereas if you are in the camp that believes that US companies pay their fair share (or more), you may find 26.43% to be your preferred estimate. Past versus Future: Investors and companies often base their future predictions on the past, and while that is entirely understandable, there is a reason why every investment pitch comes with the disclaimer that “past performance is not a reliable indicator of future performance”. I have written about how mean reversion is at the heart of many active investing strategies, and why assuming that history will repeat can be a mistake. Thus, as you peruse my historical data on implied equity risk premiums or PE ratios for the S&P 500 over time, you may be tempted to compute averages and use them in your investment strategies, or use my industry averages for debt ratios and pricing multiples as the target for every company in the peer group, but you should hold back. The Sample It is undeniable that data is more accessible and available than ever before, and I am a beneficiary. I draw my data from many raw data sources, some of which are freely available to everyone, some of which I pay for and some of which I have access to, because I work at a business school in a university. For company data, my primary source is S&P Capital IQ, augmented with data from a Bloomberg terminal. For the segment of my data that is macroeconomic, my primary source is FRED, the data set maintained by the Federal Reserve Bank, but I supplement with other data that I found online, including NAIC for bond spread data and Political Risk Services (PRS) for country risk scores. My dataset includes all publicly traded companies listed at the start of the year, with a market price available, and there were 47810 firms in my sample, roughly in line with the sample sizes in the last few years. Not surprisingly, the company listings are across the world, and I look at the breakdown of companies, by number and market cap, by geography: As you can see, the market cap of US companies at the start of 2025 accounted for roughly 49% of the market cap of global stocks, up from 44% at the start of 2024 and 42% at the start of 2023. In the table below, we compare the changes in regional market capitalizations (in $ millions) over time. Breaking down companies by (S&P) sector, again both in numbers and market cap, here is what I get: While industrials the most listed stocks, technology accounts for 21% of the market cap of all listed stocks, globally, making it the most valuable sector. Thee are wide differences across regions, though, in sector breakdown: Much of the increase in market capitalization for US equities has come from a surging technology sector, and it is striking that Europe has the lowest percent of value from tech companies of any of the broad subgroups in this table. I also create a more detailed breakdown of companies into 94 industry groups, loosely structured to stay with industry groupings that I originally created in the 1990s from Value Line data, to allow for comparisons across time. I know that this classification is at odds with the industry classifications based upon SIC or NAICS codes, but it works well enough for me, at least in the context of corporate finance and valuation. For some of you, my industry classifications may be overly broad, but if you want to use a more focused peer group, I am afraid that you will have to look elsewhere. The industry averages that I report are also provided using the regional breakdown above. If you want to check out which industry group a company falls into, please click on this file (a very large one that may take a while to download) for that detail. The Variables The variables that I report industry-average statistics for reflect my interests, and they range the spectrum, with risk, profitability, leverage, and dividend metrics thrown into the mix. Since I teach corporate finance and valuation, I find it useful to break down the data that I report based upon these groupings. The corporate finance grouping includes variables that help in the decisions that businesses need to make on investing, financing and dividends (with links to the US data for 2025, but you can find more extensive data links here.) table.tableizer-table { font-size: 12px; border: 1px solid #CCC; font-family: Arial, Helvetica, sans-serif; } .tableizer-table td { padding: 4px; margin: 3px; border: 1px solid #CCC; } .tableizer-table th { background-color: #104E8B; color: #FFF; font-weight: bold; } Corporate Governance & Descriptive 1. Insider, CEO & Institutional holdings 2. Aggregate operating numbers 3. Employee Count & Compensation Investing Principle Financing Principle Dividend Principle Hurdle RateProject ReturnsFinancing MixFinancing TypeCash ReturnDividends/Buybacks 1. Beta & Risk1. Return on Equity1. Debt Ratios & Fundamentals1. Debt Details1. Dividends and Potential Dividends (FCFE)1.Buybacks 2. Equity Risk Premiums2. Return on (invested) capital2. Ratings & Spreads2. Lease Effect2. Dividend yield & payout 3. Default Spreads3. Margins & ROC3. Tax rates 4. Costs of equity & capital4. Excess Returns on investments 4. Financing Flows 5. Market alpha (If you have trouble with the links, please try a different browser) Many of these corporate finance variables, such as the costs of equity and capital, debt ratios and accounting returns also find their way into my valuations, but I add a few variables that are more attuned to my valuation and pricing data needs as well. table.tableizer-table { font-size: 12px; border: 1px solid #CCC; font-family: Arial, Helvetica, sans-serif; } .tableizer-table td { padding: 4px; margin: 3px; border: 1px solid #CCC; } .tableizer-table th { background-color: #104E8B; color: #FFF; font-weight: bold; } Valuation Pricing Growth & ReinvestmentProfitabilityRiskMultiples 1. Historical Growth in Revenues & Earnings1. Profit Margins1. Costs of equity & capital1. Earnings Multiples 2. Fundamental Growth in Equity Earnings2. Return on Equity2. Standard Deviation in Equity/Firm Value2. Book Value Multiples 3. Fundamenal Growth in Operating Earnings 3. Revenue Multiples 4. Long term Reinvestment (Cap Ex & Acquisitons) 4. EBIT & EBITDA multiples 5. R&D 6. Working capital needs (If you have trouble with the links, please try a different browser) Not that while much of this data comes from drawn from financial statements, some of it is market-price driven (betas, standard deviations, trading data), some relates to asset classes (returns on stocks, bonds, real estate) and some are macroeconomic (interest rates, inflation and risk premiums). While some of the variables are obvious, others are subject to interpretation, and I have a glossary, where you can see the definitions that I use for the accounting variables. In addition, within each of the datasets (in excel format), you will find a page defining the variables used in that dataset. The Timing These datasets were all compiled in the last four days and reflect data available at the start of 2025. For market numbers, like market capitalization, interest rates and risk premiums, these numbers are current, reflecting the market's judgments at the start of 2025. For company financial numbers, I am reliant on accounting information, which gets updated on a quarterly basis. As a consequence, the accounting numbers reflect the most recent financial filings (usually September 30, 2024), and I use the trailing 12-month numbers through the most recent filing for flow numbers (income statement and cash flow statements) and the most recent balance sheet for stock numbers (balance sheet values). While this practice may seem inconsistent, it reflects what investors in the market have available to them, to price stocks. After all, no investor has access to calendar year 2024 accounting numbers at the start of 2025, and it seems entirely consistent to me that the trailing PE ratio at the start of 2025 be computed using the price at the start of 2025 divided by the trailing income in the twelve months ending in September 2024. In the same vein, the expected growth rates for the future and earnings in forward years are obtained by looking at the most updated forecasts from analysts at the start of 2025. Since I update the data only once a year, it will age as we go through 2025, but that aging will be most felt, if you use my pricing multiples (PE, PBV, EV to EBITDA etc.) and not so much with the accounting ratios (accounting returns). To the extent that interest rates and risk premiums will change over the course of the year, the data sets that use them (cost of capital, excess returns) allow for updating these macro numbers. In short, if the ten-year treasury rate climbs to 5% and equity risk premiums surge, you can update those numbers in the cost of capital worksheet, and get updated values. The Estimation Process While I compute the data variables by company, I am restricted from sharing company-specific data by my raw data providers, and most of the data I report is at the industry level. That said, I have wrestled with how best to estimate and report industry statistics, since almost every statistical measure comes with caveats. For a metric like price earnings ratios, computing an average across companies will result in sampling bias (from eliminating money-losing firms) and be skewed by outliers in one direction (mostly positive, since PE ratios cannot be negative). Since this problem occurs across almost all the variables, I use an aggregated variant, where with PE, for instance, I aggregate the market capitalization of all the companies (including money losing firms) in an industry grouping and divide by the aggregated net income of all the companies, including money losers. Since I include all publicly traded firms in my sample, with disclosure requirements varying across firms, there are variables where the data is missing or not disclosed. Rather than throw out these firms from the sample entirely, I keep them in my universe, but report values for only the firms with non-missing data. One example is my data on employees, a dataset that I added two years ago, where I report statistics like revenue per employee and compensation statistics. Since this is not a data item that is disclosed voluntarily only by some firms, the statistics are less reliable than on where there is universal disclosure. On an upbeat note, and speaking from the perspective of someone who has been doing this for a few decades, accounting standards around the world are less divergent now than in the past, and the data, even in small emerging markets, has far fewer missing items than ten or twenty years ago. Accessing and Using the Data The data that you will find on my website is for public consumption, and I have tried to organize it to make it easily accessible on my webpage. Note that the current year’s data can be accessed here: https://pages.stern.nyu.edu/~adamodar//New_Home_Page/datacurrent.html If you click on a link and it does not work, please try a different browser, since Google Chrome, in particular, has had issues with downloads on my server. If you are interested in getting the data from previous years, it should be available in the archived data section on my webpage: https://pages.stern.nyu.edu/~adamodar//New_Home_Page/dataarchived.html This data goes back more than twenty years, for some data items and for US data, but only a decade or so for global markets. Finally, the data is intended primarily for practitioners in corporate finance and valuation, and I hope that I can save you some time and help in valuations in real time. It is worth emphasizing that every data item on my page comes from public sources, and that anyone with time and access to data can recreate it. For a complete reading of data usage, try this link: https://pages.stern.nyu.edu/~adamodar//New_Home_Page/datahistory.html If you are in a regulatory or legal dispute, and you are using my data to make your case, you are welcome to do so, but please do not drag me into the fight. As for acknowledgements when using the data, I will repeat that I said in prior years. If you use my data and want to acknowledge that usage, I thank you, but if you skip that acknowledgement, I will not view it as a slight, and I certainly am not going to threaten you with legal consequences. As a final note, please recognize that this I don't have a team working for me, and while that gives me the benefit of controlling the process, unlike the pope, I am extremely fallible. If you find mistakes or missing links, please let me know and I will fix them as quickly as I can. Finally, I have no desire to become a data service, and I cannot meet requests for customized data, no matter how reasonable they may be. I am sorry! YouTube Video Links Current data (start of 2025) Archived data (from prior years) Companies/Industries Data definitions Data Updates for 2025 Data Update 1 for 2025: The Draw (and Danger) of Data! Data Update 2 for 2025: The Party continued for US Equities Data Update 3 for 2025: The times they are a'changin'! Data Update 4 for 2025: Interest Rates, Inflation and Central Banks!
I am a teacher at heart, and every year, for more than two decades, I have invited people to join me in the classes that I teach at the Stern School of Business at New York University. Since I teach these classes only in the spring, and the first sessions for each of the classes will be in late January, I think this is a good time to provide some details on the classes, including content and structure. If you have read these missives in prior years, much of what I say will sound familiar, but I have added new content and updated the links you will need to partake in the classes. My Motives for Teaching I was in the second year of my MBA program at UCLA, when I had my moment on grace. I had taken a job as a teaching assistant, almost entirely because I needed the money to pay my tuition and living expenses, and in a subject (accounting) that did not excite me in the least. A few minutes after I walked in to teach my first class, I realized that I had found what I wanted to do for the rest of my life, and I have been a teacher ever since. Since that was 1983, this will be my forty first year teaching, and I have never once regretted my choice. I know that teaching is not a profession held in high esteem anymore, for good and bad reasons, and I will not try to defend it here. It is possible that some of the critics are right, and I teach because I cannot do, but I like to think that there is more to my career choice than ineptitude. My motivations for teaching are manifold, and let me list some of them: I like the stage: I believe that every teacher, to some extent, has a little bit of a repressed actor in him or her, and I do enjoy being in front of an audience, with the added benefit that I get to review the audience, with the grades that I given them, rather than the other way around. I like to make a difference: I do not expect my students to agree with all or even much of what I have to say, but I would like to think that I sometimes change the way they think about finance, and perhaps even affect their choice of professions. I am lucky enough to hear from students who were in my classes decades ago, and to find out that my teaching made a difference in their lives. I like not having a boss: I would be a terrible employee, since I am headstrong, opinionated and awfully lazy, especially when I must do things I don’t like to do. As a teacher, I am my own boss and find my foibles completely understandable and forgivable. I know that teaching may not be your cup of tea, but I do hope that you enjoy whatever you do, as much as I do teaching, and I would like to think that some of that joy comes through. My Teaching Process I do a session on how to teach for business school faculty, and I emphasize that there is no one template for a good teacher. I am an old-fashioned lecturer, a control freak when it comes to what happens in my classroom. In forty years of teaching, I have never once had a guest lecturer in my classroom or turned my class over to a free-for-all discussion. Class narrative: This may be a quirk of mine, but I stay away from teaching classes that are collections of topics. In my view, having a unifying narrative not only makes a class more fun to teach, but also more memorable. As you look at my class list in the next section, you will note that each of the classes is built around a story line, with the sessions building up to what is hopefully a climax. Bulking up the reasoning muscle: When asked a question in class, even if I know the answer, I try to not only reason my way to an answer, but to also be open about doubts that I may have about that answer. In keeping with the old saying that it is better to teach someone to fish, than to give them fish, I believe it is my job to equip my students with the capacity to come up with answers to questions that they may face in the future. In my post on the threat that AI poses to us, I argued that one advantage we have over AI is the capacity to reason, but that the ease of looking up answers online, i.e., the Google search curse, is eating away at that capacity. Make it real: I know that, and especially so in business schools, students feel that what they are learning will not work in the real world. I like to think that my classes are firmly grounded in reality, with my examples being real companies in real time. I am aware of the risks that when you work with companies in real time, your mistakes will also play out in real time, but I am okay with being wrong. Straight answers: When I was a student, I remember being frustrated by teachers, who so thoroughly hedged themselves, with the one hand and the other hand playing out, that they left me unclear about what they were saying. I would like to think that I do not hold back, and that I stay true to the motto that I would rather be transparently wrong than opaquely right. It has sometimes got me some blowback, when I expressed my views about value investing being rigid, ritualistic and righteous and the absolute emptiness of virtue concepts like ESG and sustainability, but so be it. I am aware of things that I need to work on. My ego sometimes still gets in the way of admitting when I am wrong, I often do not let students finish their questions before answering them, I am sometimes more abrupt (and less kind) than I should be, especially when I am trying to get through material and my jokes can be off color and corny (as my kids point out to me). I do keep working on my teaching, though, and if you are a teacher, no matter what level you teach at, I think of you as a kindred spirit. My Class Content In my first two years of teaching, from 1984 to 1986, I was a visiting professor at the University of California at Berkeley, and like many visiting faculty around the world, I was asked to plug in holes in the teaching schedule. I taught six different classes ranging from a corporate finance class to undergraduates to a central banking for executive MBAs, and while I spent almost all of my time struggling to stay ahead of my students, with the material, it set me on a pathway to being a generalist. Once I came to NYU in 1986, I continued to teach classes across the finance spectrum, from corporate finance to valuation to investing, and I am glad that I did so. I am a natural dabbler, and I enjoy looking at big financial questions and ideas from multiple perspectives. There are two core classes that I have taught to the MBAs at Stern, almost every year since 1986. The first is corporate finance, a class about the first principles that should govern how to run a business, and thus a required class (in my biased view) for everyone in business. If you are a business owner or operator, this class should give you the tools to use to make business choices that make the most financial sense. If you work in a business, whether it be in marketing, strategy or HR, this class is designed to provide perspective on how what you do fits into value creation at your business. If you are just interested in business, just as an observer, you may find this class useful in examining why companies do what they do, from acquisitions to buybacks, and when corporate actions violate common sense. The second is valuation, a class about how to value or price almost anything, with a tool set for those who need to put numbers on assets. Again, I teach this class to a broad audience, from appraisers/analysts whose jobs revolve around valuation/pricing to portfolio managers who are often users of analyst valuations to business owners, whose interests in valuation can range from curiosity (how much is my business worth?) to the transactional (how much of my business should I give up for a capital infusion?) While my class schedule has been filled with these two courses, I developed a third course, investment philosophies, a class about how to approach investing, trying to explain why investors with very different market views and investment strategies can co-exist in a market, and why there is no one philosophy that dominates. My endgame for this class is to provide as unbiased a perspective as I can for a range of philosophies from trading on price patterns to market timing, with stops along the way from value investing, growth investing and information trading. It is my hope that this class will allow you to find the investment philosophy that best fits you, given your financial profile and psychological makeup. In 2024, I added a fourth course to the mix, one centered around my view that businesses age like human beings do, i.e., there is a corporate life cycle, and that how businesses operate and how investors value them, changes as they move from youth to demise. I have used the corporate life cycle perspective to structure my thinking on almost every class that I teach, and in this class, I isolate it to examine how businesses age and how they respond to to aging, sometimes in destructive ways. In my corporate finance and valuation classes, the raw material comes from financial statements, and I realized early on that my students, despite having had a class or two on accounting, still struggled with reading and using financial statements, and I created a short accounting class, specifically designed with financial analysis and valuation in mind. The class is structured around the three financial statements that embody financial reporting - the income statement, balance sheet and statement of cash flows - and how the categorization (and miscategorization) of expenses into operating, financing and capital expenses plays out in these statements. As many of you who may have read my work know, I think that fair value accounting is not just an oxymoron but one that has done serious damage to the informativeness of financial statements, and I use this class to explain why. Since so much of finance is built around the time value of money (present value) and an understanding of financial markets and securities, I also have a short online foundational class in finance: As you can see, this class covers the bare basics of macroeconomics, since that is all I am capable to teaching, but in my experience, it is all that I have needed in finance. As our access to financial data and tools has improved, I added a short course on statistics, again with the narrow objective of providing the basic tools of data analysis. A statistics purist would probably blanch at my treatment of regressions, correlations and descriptive statistics, but as a pragmatist, I am willing to compromise and move along. As you browse through the content of these classes, and consider whether you want to take one, it is worth noting that they are taught in different formats. The corporate finance and valuation classes will be taught in the spring, starting in late January and ending in mid-May, with two eighty-minute sessions each week that will be recorded and accessible shorts after they are delivered in the classroom. There are online versions of both classes, and the investment philosophies class, that take the form of shorter recorded online classes (about twenty minutes), that you can either take for free on my webpage or for a certificate from NYU, for a fee. The accounting, statistics and foundations classes are only in online format, on my webpage, and they are free. All in all, I know that some of you are budget-constrained, and others of you are time-constrained, and I hope that there is an offering that meeting your constraints. If you are interested, the table below lists the gateways to each of the classes listed above. Note that the links for the spring 2025 classes will lead you to webcast pages, where there are no sessions listed yet, since the classes start in late January 2025. The links to the NYU certificate classes will take you to the NYU page that will allow you to enroll if you are interested, but for a price. The links to the free online classes will take you to pages that list the course sessions, with post-class tests and material to go with each session: table.tableizer-table { font-size: 12px; border: 1px solid #CCC; font-family: Arial, Helvetica, sans-serif; } .tableizer-table td { padding: 4px; margin: 3px; border: 1px solid #CCC; } .tableizer-table th { background-color: #104E8B; color: #FFF; font-weight: bold; } ClassNYU Spring 2025 Online (free)NYU CertificateWhatsApp Discussion Group Corporate FinanceLinkLinkLink (Fall)Link ValuationLinkLinkLink (Spring & Fall)Link Investment PhilosophiesNALinkLink (Spring)Link Corporate Life CycleNALinkNALink AccountingNALinkNA Foundations of FinanceNALinkNA StatisticsNALinkNA The last column represents WhatsApp groups that I have set up for each class, where you can raise and answer questions from others taking the class. My Book (and Written) Content Let me begin by emphasizing that you do not need any of my books to take my classes. In fact, I don't even require them, when I teach my MBA and undergraduate classes at NYU. The classes are self contained, with the material you need in the slides that I use for each class, and these slides will be accessible at no cost, either as a packet for the entire class or as a link to the session (on YouTube). To the extent that I use other material, spreadsheets or data in each session, the links to those as well will be accessible as well. If you prefer to have a book, I do have a few that cover the classes that I teach, though some of them are obscenely overpriced (in my view, and there is little that I can do about the publishing business and its desire for self immolation.) You can find my books, and the webpages that support these books, at this link, and a description of the books is below: table.tableizer-table { font-size: 12px; border: 1px solid #CCC; font-family: Arial, Helvetica, sans-serif; } .tableizer-table td { padding: 4px; margin: 3px; border: 1px solid #CCC; } .tableizer-table th { background-color: #104E8B; color: #FFF; font-weight: bold; } Corporate Finance Valuation Investment Philosophies Corporate Life Cycle Applied Corporate Finance (Wiley, 4th Ed): This is the book that is most closely tied to this class and represents my views of what should be in a corporate finance class most closely. Investment Valuation (Wiley, 3rd Ed, 4th ed forthcoming): This is my only valuation textbook, designed for classroom teaching. At almost 1000 pages, it is overkill but it is also the most comprehensive of the books in terms of coverage. Investment Philosophies (Wiley, 2nd Ed): This is the best book for this class, and provides background and evidence for each investment philosophy, with a listing of the personal characteristics that you need to make that philosophy work for you. Corporate Life Cycle (Penguin Random House, 1st Ed): This is the most recent of my books and it introduces the phases of the corporate life cycle and why business, management, valuation and investment challenges change with each phase. Corporate Finance (Wiley, 2nd Ed): This is a more conventional corporate finance book, but it has not seen a new edition in almost 20 years. Little Book of Valuation (Wiley, 2nd Ed): This is the shortest of the books, but it provides the essentials of valuation, and at a reasonable price. Investment Management (Wiley, 1st Ed): This is a very old book, and one that I co-edited with the redoubtable Peter Bernstein, focused on writings on different parts of the investment process. It is dated but it still has relevance (in my view). Strategic Risk Taking (Wharton, 1s Ed): This is a book specifically about measuring risk, dealing with risk and how risk taking/avoidance affect value. Dark Side of Valuation (Prentice Hall, 3rd Ed): This is a book about valuing difficult-to-value companies, from young businesses to cyclical/commodity companies. It is a good add-on to the valuation class. Investment Fables (FT Press, 1st Ed): This book is also old and badly in need of a second edition, which I may turn to next year, but it covers stories that we hear about how to beat the market and get rich quickly, the flaws in these stories, and why it pays to be a skeptic. Damodaran on Valuation (Wiley, 2nd Ed): This was my very first book, and it is practitioner-oriented, with the second half of the book dedicated to loose ends in vlauation (control, illiquidity etc.) Narrative and Numbers (Columbia Press, 1st Ed): This was the book I most enjoyed writing, and it ties storytelling to numbers in valuation, providing a basis for my argument that every good valuation is a bridge between stories and numbers. Finally, I discovered early on how frustrating it is to be dependent on outsiders for data that you need for corporate financial analysis and valuation, and I decided to become self sufficient and create my own data tables, where I report industry averages on almost every statistic that we track and estimate in finance. These data tables should be accessible and downloadable (in excel), and if you find yourself stymied, when doing so, trying another browser often helps. The data is updated once a year, at the start of the year, and the 2025 data update will be available around January 10, 2025. A Class Guide I would be delighted, if you decide to take one or more of my classes, but I understand that your lives are busy, with jobs, family and friends all competing for your time. You may start with the intent of taking a course, but you may not be able to finish for any number of reasons, and if that happens, I completely understand. In addition, the courses that you find useful will depend on your end game. If you own a business, work in the finance department of a company, or are a consultant, you may find the corporate finance course alone will suffice, providing most of what you need. If you are in the appraisal or valuation business, either as an appraiser or as an equity research analyst (buy or sell side), valuation is the class that will be most directly tied to what you will do. I do believe that to value businesses, you need to understand how to run them, making corporate finance a good lead in. If you plan to be in active investment, working at a mutual fund, wealth management or hedge fund, or are an individual investor trying to find your way in investing, I think that starting with a valuation class, and following up with investment philosophy will yield the biggest payoff. Finally, the corporate life cycle class, which spans corporate finance, valuation and investing, with doses of management and strategy, will be a good add on to any of the other pathways, or as a standalone for someone who has little patience for finance classes but wants a framework for understanding businesses. As a lead-in to any of these paths, I will leave it to you to decide whether you need to take the accounting, statistics, and foundations classes, to either refresh content you have not seen in a long time or because you find yourself confused about basics: If you find yourself overwhelmed with any or all of these paths, you always have the option of watching a session or two of any class of your choice. As you look at the choices, you have to consider three realities. The first is that, unless you happen to be a NYU Stern student, you will be taking these classes online and asynchronously (not in real time). As someone who has been teaching online for close to two decades now, I have learned that watching a class on a computer or display screen is far more draining than being in a physical class, which is one reason that I have created the online versions of the classes with much shorter session lengths. The second is that the biggest impediment to finishing classes online, explaining why completion rates are often 5% or lower, even for the best structured online classes, is maintaining the discipline to continue with a class, when you fall behind. While my regular classes follow a time line, you don't have to stick with that calendar constraint, and can finish the class over a longer period, if you want, but you will have to work at it. The third is that learning, especially in my subject area, requires doing, and if all you do is watch the lecture videos, without following through (by trying out what you have learned on real companies of your choosing), the material will not stick. I will be teaching close to 800 students across my three NYU classes, in the spring, and they will get the bulk of my attention, in terms of grading and responding to emails and questions. With my limited bandwidth and time, I am afraid that I will not be able to answer most of your questions, if you are taking the free classes online; with the certificate classes, there will be zoom office hours once every two weeks for a live Q&A. I have created WhatsApp forums (see class list above) for you, if you are interested, to be able to interact with other students who are in the same position that you are in, and hopefully, there will be someone in the forum who can address your doubts. Since I have never done this before, it is an experiment, and I will shut them down, if the trolls take over. In Closing… I hope to see you (in person or virtually) in one of my classes, and that you find the content useful. If you are taking one of my free classes, please recognize that I share my content, not out of altruism, but because like most teachers, I like a big audience. If you are taking the NYU certificate classes, and you find the price tag daunting, I am afraid that I cannot do much more than commiserate, since the university has its own imperatives. If you do feel that you want to thank me, the best way you can do this is to pass it on, perhaps by teaching someone around you. YouTube Video Class list with links Corporate Finance (NYU MBA): https://pages.stern.nyu.edu/~adamodar/New_Home_Page/webcastcfspr25.htm Valuation (NYU MBA): https://pages.stern.nyu.edu/~adamodar/New_Home_Page/webcasteqspr25.htm Corporate Finance (Free Online): https://pages.stern.nyu.edu/adamodar/New_Home_Page/webcastcfonline.htm Valuation (Free Online): https://pages.stern.nyu.edu/~adamodar/New_Home_Page/webcastvalonline.htm Corporate Finance (NYU Certificate): https://execed.stern.nyu.edu/products/corporate-finance-with-aswath-damodaran Valuation (NYU Certificate): https://execed.stern.nyu.edu/products/advanced-valuation-with-aswath-damodaran Investment Philosophies (Free Online): https://pages.stern.nyu.edu/~adamodar/New_Home_Page/webcastinvphil.htm Investment Philosophies (NYU Certificate): https://execed.stern.nyu.edu/products/investment-philosophies-with-aswath-damodaran Corporate Life Cycle (Free Online): https://pages.stern.nyu.edu/~adamodar/New_Home_Page/webcastCLC.htm Accounting 101 (Free Online): https://pages.stern.nyu.edu/~adamodar/New_Home_Page/webcastacctg.htm Foundations of Finance (Free Online): https://pages.stern.nyu.edu/~adamodar/New_Home_Page/webcastfoundationsonline.htm Statistics 101 (Free Online): https://pages.stern.nyu.edu/~adamodar/New_Home_Page/webcaststatistics.htm WhatsApp Groups for Classes Corporate Finance: https://chat.whatsapp.com/C0yjIAWT2WdLozCHYctU9p Valuation: https://chat.whatsapp.com/LjQBQXcbyh11I17idz176k Investment Philosophies: https://chat.whatsapp.com/IolVsa3qScLJecUtu4uUKO Corporate Life Cycle: https://chat.whatsapp.com/J1V0vwFkIUoCblYp4J3ENs
You might know, by now, of my views on ESG, which I have described as an empty acronym, born in sanctimony, nurtured in hypocrisy and sold with sophistry. My voyage with ESG began with curiosity in my 2019 exploration of what it purported to measure, turned to cynicism as the answers to the Cui Bono (who benefits) question became clear and has curdled into something close to contempt, as ESG advocates rewrote history and retroactively changed their measurements in recent years. Late last year, I looked at impact investing, as a subset of ESG investing, and chronicled the trillions put into fighting climate change, and the absence of impact from that spending. Sometime before these assessments, I also looked at the notion of stakeholder wealth maximization as an idea that only corporate lawyers and strategists would love, and argued that there is a reason, in conventional businesses to stay focused on shareholders. With each of these topics (ESG, impact investing, stakeholder wealth maximization), the response that I got from some of the strongest defenders was that "sustainability" is the ultimate end game, and that the fault has been in execution (in ESG and impact investing), and not in the core idea. I was curious about what sets sustainability apart from the critiqued ideas, as well as skeptical, since the cast of characters (individual and entities) in the sustainability sales pitch seems much the same as for the ESG and impact investing sales pitches. In critiquing sustainability, I may be swimming against the tide, but less so than I was five years ago, when I first wrote about these issues. In fact, in my first post on ESG, I confessed that I risked being labeled as a "moral troglodyte" for my views, and I am sure that my subsequent posts have made that a reality, but I have a thick skin. This post on sustainability will, if it is read, draw withering scorn from the righteous, and take me off their party invite list, but I don't like parties anyway. Sustainability: The What, the Why and the Who? I have been in business and markets for more than four decades, and while sustainability as an end game has existed through that period, but much of that time, it was in the context of the planet, not for businesses. It is in the last two decades that corporate sustainability has become a term that you see in academic and business circles, albeit with definitions that vary across users. Before we look at how those definitions have evolved, it is instructive to start with three measures of sustainability, measuring (in my view) very different things: Planet sustainability, measuring how our actions, as consumers and businesses, affect the planet, and our collective welfare and well being. This, of course, covers everything from climate change to health care to income inequality. Product sustainability, measuring how long a product or service from a business can be used effectively, before becoming useless or waste. In a throw-away world, where planned obsolescence seems to be built into every product or service, there are consumers and governments who care about product sustainability, albeit for different reasons. Business or corporate sustainability, measuring the life of a business or company, and actions that can extend or constrict that life. There are corporate sustainability advocates who will argue that it covers all of the above, and that a business that wants to increase its sustainability has to make more sustainable products, and that doing so will improve planet sustainability. That may be true, in some cases, but in many, there will be conflicts. A company that makes shaving razors may be able to create razor blades that stay sharp forever, and need no replacement, but that increased product sustainability may crimp corporate sustainability. In the same vein, there may be some companies (and you can let your priors guide you in naming them), whose very existence puts the planet at risk, and if planet sustainability is the end game, the best thing that can happen is for these companies to cease to exist. Which of these measures of sustainability lies at the heart of corporate sustainability, as practiced today? To get the answers, I looked at a variety of players in the sustainability game, and will use their own words in the description, lest I be accused of taking them out of context: Business schools around the world have discovered that sustainability classes not only draw well, and improve their rankings (especially with the Financial Times, which seems to have a fetish with the concept), but are also money makers when constructed as executive classes. NYU, the institution that I teach at, has an executive corporate sustainability course, with certification costing $2,200, but I will quote the Vanderbilt University course description instead, where for a $3,000 price tag, you can get a certificate in corporate sustainability, which is described as " a holistic approach to conducting business while achieving long-term environmental, social, and economic sustainability." Academia: I read through seminal and impactful (as academics, we are fond of both words, with the latter measured in citations) papers on corporate sustainability, to examine how they defined and measured sustainability. A 2003 paper on corporate sustainability describes it as recognizing that "corporate growth and profitability are important, it also requires the corporation to pursue societal goals, specifically those relating to sustainable development — environmental protection, social justice and equity, and economic development." In the last two decades, it is estimated that there have been more than twelve thousand articles published on corporate sustainability, and while the definition has remained resilient, it has developed offshoots and variants. Corporate/Business: Companies, around the world, were quick to jump onto the sustainability bandwagon, and sustainability (or something to that effect) is part of many corporate mission statements. The Hartford, a US insurance company, describes corporate sustainability as centered "around developing business strategies and solutions to serve the needs of our stakeholders, while embracing the necessary innovation and foresight to ensure we are able to meet those needs in the decades to come." Governments: Governments have also joined the party, and the EU has been the frontrunner, and its definition of corporate sustainability as "integrating social, environmental, ethical, consumer, and human rights concerns into their business strategy and operations" has become the basis for both disclosure and regulatory actions. The Canadian government has used to EU model to create a corporate sustainability reporting directive, requiring companies to report on and spend more on a host on environmental, social and governance indicators. I am willing to be convinced otherwise, but all of these definitions seem to be centered around planet sustainability, with varying motivations for why businesses should act on that front, from clean consciences (it is the right thing to do) to being "good for business" (if you do it, you will become more profitable and valuable). While corporate sustainability has taken center stage in the last two decades, it is part of a discussion about the social responsibilities of businesses that has been around for centuries. From Adam Smith's description of economics as the "gospel of mammon" in the 1700s to Milton Friedman's full-throated defense of business in the 1970s, it can be argued that almost every debate about businesses has included discussions of what they should do for society, beyond just following the law. That said, corporate sustainability (and its offshoots) have clearly taken a more central role in business than ever before, and one manifestation is in the rise of "corporate sustainability officers" (CSOs) at many large companies. A PwC survey of 1640 companies in 62 countries, in 2022, found that the number of companies with CSOs tripled in 2021, with about 30% of all companies having someone in that position. A Conference Board survey of hundred sustainability leaders (take the sample bias into account) of the state of corporate sustainability pointed to the expectation that sustainability teams at companies would continue to grow over time. Finally, going back to academia, an indicator of the buzz in buzzwords, a survey paper in 2022 noted the rise in the number of corporate-sustainability related articles in recent years, as well as documenting their focus: Burbano, Delma and Cobo (2022) Note that much of the surge in articles came from ESG, which at least for the bulk of this period marched in lockstep with sustainability. Reflecting that twinning, many of the papers on corporate sustainability, just like the papers on ESG, were framed as sustainability being not just good for society but also good for the companies that adopted them. I will admit that I have no idea what a CSO is or does, but I did get a chance to find out for myself, when I was invited to give a talk to the CSOs of fifty large companies. I started that session with a question, born entirely out of curiosity, to the audience of what they did, at their respective organizations. After about twenty minutes of discussion, it was very clear that there was no consensus answer. In fact, some were as in the dark, as I was, about a CSO's responsibilities and role, and among the many and sometimes convoluted and contradictory answers I heard, here was my categorization of potential CSO roles: CSO as Yoda: Some of the CSOs described their role as providing vision and guidance to the companies they worked at, about the societal effects of their actions, and doing so with a long term perspective. In short, even though they did not make this explicit, they were projecting that they had the training and foresight on how the company and society would evolve over time, and advice the company on the actions that it would need to take to match that evolution. I was tempted, though I restrained myself, to ask what training they had to be such receptacles of wisdom, since a degree or certification in sustainability clearly would not do the trick. I did dig into Star Wars lore, where it is estimated that it takes a decade or two of intense training to become a Jedi, and left open the possibility that there may be an institution somewhere that is turning out sustainability jedis. CSO as Jiminy Cricket: I am a fan of Disney movies, and Pinocchio, while not one of the best known, remains one of my favorites. If you have watched the movie, Jiminy Cricket is the character that sits on Pinocchio's shoulder and acts as his conscience, and for some of the CSOs in the audience, that seemed to be the template, i.e., to act as corporate consciences, reminding the companies that they work for, of the social effects of their actions. The problem, of course, is that like the Jiminy Cricket in the movie, they get tagged as relentless scolds, usually get ignored, and get little glory, even when proved right. CSO as PR Genius: There were a few CSOs who were open about the fact that they were effectively marketing fronts for companies, with the job of taking actions that could not remotely be argued as being good for the planet and selling them as such. I am not sure whether Unilever's CSO was involved in the process, but the company's push to have each of its four hundred brands have a social or environmental purpose would have fallen into this realm. CSO as Embalmer: Finally, there were some CSOs who argued that it was their job to ensure that the company would live longer, perhaps even forever. If you are familiar with my work on corporate life cycles, I believe that not much good comes from companies surviving as “walking dead” entities, but in a world where survival at any cost is viewed as success, it is a by product. Here are the roles in table form, with the training that would prepare you best for each one: I am sure that I am missing some of the nuance in sustainability, but if so, remember that nuance does not survive well in business contexts, where a version of Gresham's law is at work, with the worst motives driving out the best. Sustainability and ESG In the last two or three years, corporate sustainability advocates have tried to distance themselves from ESG, arguing that the faults of ESG are of its own doing, and came from ignoring sustainability lessons. I am sorry, but I don't buy it. If ESG did not exist, sustainability would have had to invent it, because much of the growth in sustainability as a money-maker has come from its ESG arm. As I see it, ESG took the abstractions of corporate sustainability and converted them into a score, and it was that much maligned scoring mechanism that caused a surge of adoptions both in corporate boardrooms and among the investment community. It is worth noting that both ESG and sustainability draw their rationale from stakeholder wealth maximization, with the core thesis being that businesses should be run for the benefit of all stakeholders, rather than “just” for shareholders. It is in this context that I used the "theocratic trifecta" to describe how ESG, sustainability and stakeholder wealth are linked, and have been marketed. I use the word “theocratic” deliberately, since like theocrats in every domain, some in the sustainability space believe that they own the high ground on virtue, and view dissent as almost sacrilegious. While a scoring mechanism, by itself, can be viewed as having a good purpose, i.e., to create a measure of how much a company is moving towards it sustainability goals, and to hold it accountable, it creates natural consequences that come with all scoring mechanisms: Measurers claiming to be objective arbiters, when the truth is that all scores require subjective judgments about what comprises goodness, and the consequences for business profitability and value. Businesses that start to understand the scoring process and factors, and then game the scoring systems to improve their scores. Greenwashing is a feature of these scoring systems, not a bug, and the more you try to refine the scoring, the more sophisticated the gaming will become. Advocates wringing their hands about the gaming, and arguing that the answer is more detailed definitions of things that defy definition, not recognizing (or perhaps not caring) that this just feeds the cycle and creates even more gaming. With ESG, we have seen this process play out in destructive ways, with the scoring services (Sustainalytics, S&P, Refinitiv) using not only different criteria to come up with scores, but also changing those criteria in time and companies with the most resources to do so gaming those scoring systems to deliver better ESG scores. Accountants and regulators have added to the mix, by increasing disclosure requirements on almost every aspect of ESG, with little or no tangible benefits to show in terms of actual change. Taking a step back and looking at ESG and sustainability as concepts, they share many of the same characteristics: They are opaque: Both ESG and sustainability are opaque to the point of obfuscation, perhaps because it serves the interests of advocates, who can then market them in whatever form they want to. To the pushback from defenders that the details are being nailed down or that there are new standards in place or coming, the argument runs hollow because the end game seems to keep changing. With ESG, for instance, the end game when it was initiated was making the world a better place (doing good), which evolved to generating alpha (excess returns for investors), on to being a risk measure before converting on a disclosure requirement. Defenders argue that there will be convergence driven by tighter definitions from regulators and rule makers, and the EU, in particular, has been in the lead on this front, putting out a Corporate Sustainability Reporting Directive (CSRD) in 2022, outlining economic activities that contribute to meeting the EU’s environmental objectives. While ESG advocates may be right about convergence, looking to the the bureaucracy in Brussels to have the good sense (on economics and sustainability) to get this right is analogous to asking a long-time vegan where you can get the best steak in town. They are rooted in virtue: While some of the advocates for ESG and sustainability have now steered away from goodness as an argument for their use, almost every debate about the two topics eventually ends up with advocates claiming to be on the side of good, with critics consigned to the dark side. Disclosures, over actions: The path for purpose-driven concepts (sustainability, ESG) seems to follow a familiar arc. They start with the endgame of making the world a better place, are marketed with the pitch that purpose and profits go together (the original sin) and when the lie is exposed, are repackaged as being about disclosures that can be used by consumers and investors to make informed judgments. Both ESG and sustainability have traversed this path, and both seem to be approaching the "it's all about disclosure" phase of the cycle. While that seems like a reasonable outcome, since almost everyone is in favor of more information, there are two downsides to this disclosure drive. The first is that disclosure can become not just a substitute for acting, but an impediment to the change that makes a difference. The second is that as disclosures become more extensive, there is a tipping point, especially as the consequential disclosures are mixed in with minor ones, where users start ignoring the disclosure, effectively removing their information value. Underplay or ignore sacrifice: Of all the mistakes, the biggest one made in the sales pitch for ESG and sustainability was that you could eat your cake, and have it too. Companies were told that being sustainable would make them more profitable and valuable, investors were sold on the notion that investing in good companies would deliver higher or extra returns and consumers were informed that they could make sustainable choices, with little or no additional cost. The truth is that sustainability will be costly to businesses, investors, and consumers, and why should that surprise us? Through history, being good has always required sacrifice, and it was always hubris to argue that you could upend that history, with ESG and sustainability. Notwithstanding the money, time and resources that have been poured into ESG and sustainability, there is little in terms of real change on any of the societal or climate problems that they purport to want to change. Can sustainability be saved? I may be a moral troglodyte, because of my views on ESG, sustainability and all things good, but we have a shared interest in making the world a better place, and that leads to the question of whether corporate sustainability, or at least the mission that it espouses, can be salvaged. I believe that there is a path forward, but it requires steps that many sustainability purists may find anathema: Be clear eyed about what can be achieved at the business level: There is truth to the Milton Friedman adage that the business of business is business, not filling in for social needs or catering to non-business interests. It is true that there are actions that businesses take that can create costs to society, and even if the law does not require it, it behooves us to get businesses to behave better, without asking them to do what governments and regulators should be doing. For business sustainability to deliver results, that line between business and government action has to be made clearer, and adhered to in practice. Open about the costs to businesses of meeting sustainability goals: Be real about the sacrifices in profitability and value that will be needed for a company to do what's good for society. To the extent that in a publicly traded company, it is not the managers, but one of the stakeholders (shareholders, bondholders, employees or customer), who bear this cost, you need buy in from them, if the sustainability actions are voluntary. For companies that are well managed and have done well for their stakeholders, the sacrifice may be easier to sell, but for badly managed businesses, it will be, and should be, a steeper hill to climb. To the extent that corporate executives and fund managers choose to create costs for others (shareholders in a company, investors in a fund), without their buy in, there is clearly a violation of fiduciary duty that will and should leave them exposed to legal consequences. Clear about who bears these costs: I was recently asked to give testimony to a Canadian parliamentary committee that was considering ways of getting banks to contribute to fighting climate change (by lending less to fossil fuel companies and more to green energy firms), and much of what I heard from committee members and the other experts was about how banks would bear the costs. The truth is that when a bank is either restricted from a profit-making activity (lending to fossil fuel companies) or forced to subsidize a money-losing activity (lending at below-market rates to green energy companies), the costs are borne by either the bank's shareholders or depositors, or, in some cases, by taxpayers. In fact, given that bank equity is such a small slice of overall capital, it is bank depositors who will be burdened the most by bank lending mandates, and that opens the door to bank failures and worse. And honest about cost sharing: One of the benefits of recognizing that being good (for the planet or society) creates costs is that we can then also follow up by looking at who bears the costs. It is my view that for much of the past few decades, we (as academics, policy makers and regulators) been far too quick to decide what works for the "greater good", at least as we see it, and oblivious to the reality that the costs of delivering that greater good are borne by the people who can least afford it. Above all, drain the gravy train: Both ESG and sustainability have been contaminated by the many people and entities that have benefited monetarily from their existence. The path to making sustainability matter has to start by removing the grifters, many masquerading as academics and experts, from the space. I won’t name names, but if you want to see who you should be putting on that grifter list, many of them will be at the annual extravaganza called COP29, where the useful idiots and feckless knaves who inhabit this space will fly in from distant places to Azerbaijan, to lecture the rest of us on how to minimize our carbon footprint. If you are a business that cares about the planet, fire your sustainability consultants and stop bending business models to meet disclosure needs, and while you are at it, you may want to get rid of your CSO (if you have one), unless you happen to have Yoda on your payroll. In all of this discussion, there is a real problem that no one in the space seems to be willing to accept or admit to, and that is much as we (as consumers, investors and voters) claim to care about social good, we are unwilling to burden ourselves, even slightly (by paying higher prices or taxes), to deliver that good. It could be because we are callous, or have become so, but I think the true reason is that we have lost trust in experts, governments and institutions, and who can blame us? Whether it is the city of San Diego, where I live, trying to increase sales taxes by half a percent or a government imposing a carbon tax, taxpayers seem disinclined to given governments the benefit of doubt, given their history of inefficiencies and broken promises. One argument that I have heard from many advocates for ESG and sustainability is that the pushback against these ideas is coming primarily from the United States, and that much of the rest of the world has bought in to their necessity and utility. If these people leave the ivory towers and echo chambers that they inhabit, and talk to people in their own environs, they will recognize that the loss of trust is a global phenomenon, and that any consensus that exists is on the surface. There are many reasons that incumbent governments in Canada and Germany (both "leaders" in the climate change fight) are facing the political abyss in upcoming elections, but one reason is the "we know best" arrogance embedded in their climate change strictures and laws, combined with the insulting pitch that the people most affected by these laws will not feel the pain. How do we get trust in institutions back? It will not come from lecturing people on their moral shortcomings (as many will undoubtedly do to me, after reading this) or by gaslighting them (telling them that they are better off when they are clearly and materially not). It will require humility, where the agents of change (academics, governments, regulators) are transparent about what they hope to accomplish, and the costs of and uncertainties about reaching those objectives, and patience, where incremental change takes precedence over seismic or revolutionary change. YouTube Video My posts on ESG, impact investing and stakeholder wealth From Shareholder Wealth to Stakeholder Interests: CEO Capitulation or Empty Doublespeak? (August 2019) Sounding Good or Doing Good? A Skeptical Look at ESG (September 2020) The ESG Movement: The "Goodness" Gravy Train Rolls On! (September 2021) ESG's Russia Test: Trial by Fire or Crash and Burn? (March 2022) Good Intentions, Perverse Outcomes: The Impact of Impact Investing (October 2023)
In this, the first full week in November 2024, the big news stories of this week are political, as the US presidential election reached its climactic moment on Tuesday, but I don't write about politics, not because I do not have political views, but because I reserve those views are for my friends and family. The focus of my writing has always been on markets and companies, more micro than macro, and I am sure that you will find my spouting off about who I voted for, and why, off-putting, much as I did in his cycle, when celebrities and sports stars told me their voting plans. This post, though, does have a political angle, albeit with a market twist. During the just-concluded presidential election, we saw election markets, allowing you to predict almost every subset of the election, not only open up and grow, but also insert themselves into the political discourse. I would like to use this post to examine how these markets did during the lead in to the election, and then expand the discussion to a more general one of what markets do well, what they do badly, i.e., revisit an age-old divide between those who believe in the wisdom of crowds and and those that point to their madness. Election Forecasts: From polls to political markets I watched the movie "Conclave"just a couple of days ago, and it is about the death of a pope, and the meeting to pick a replacement. (It is based on a book by Robert Harris, one of my favorite authors.) In the movie, as the hundred-plus Catholic cardinals gathered in the Sistine chapel, to pick a pope, I was struck by how the leading candidates gauged support and jockeyed ahead of the election, essentially informally polling their brethren. I know that the movie (and book) is fiction, but I am sure that the actual conclaves that have characterized papal succession for centuries have used informal polling as a way of forecasting election winners for centuries. In fact, going back to the very first democracies in Greek and Roman times, where notwithstanding the restrictions on who could vote, there were attempts to assess election winners and losers, ahead of the event. The first reported example of formal polling occurred ahead of the 1824 presidential election, when the Raleigh Star and North Carolina Gazette polled 504 voters to determine (rightly) that Andrew Jackson would beat John Quincy Adams. Starting in 1916, The Literary Digest started a political survey, asking its readers, and after correctly predicting the next four elections, failed badly in 1936 (predicting that Alf Landon would beat FDR in the election that year, when, in fact, he lost in a landslide). While polling found its statistical roots after that, it had one of its early dark moments, in 1948, when pollsters predictions that Thomas Dewey would beat Harry Truman were upended on Election Day, leading to one of the most famous headlines of all time (in the Chicago Tribune). In the decades after, polling did learn valuable lessons about sampling bias and with an assist from technological advancements, and the number of pollsters has proliferated. Coming into this century, pollsters were convinced that they had largely ironed out their big problems, but even at it peak, polls came with noise (standard errors), though pollsters were not always transparent about it, and the public took polling estimates as facts. The fact that individual polls, even if not biased, are noisy (with ranges around estimates) led to a poll aggregators, which collected individual polls and averaged them out to yield presumably a more precise estimate. Here, for example, is the aggregated value from Real Clear Politics (RCP), which has been doing this for at least four presidential election cycles now, leading into election days in the US (November 5): While the original reason for aggregation was removing bias, aggregators can still induce bias by deciding which polls to include (and exclude) in their averages, and sometimes in how they weight these polls. While RCP computes simple averages, there are other aggregators who weight polls, based generally on their accuracy in prior elections, but bias enters in insidious ways. The pushback in poll-based forecasting (whether individual or aggregated) is that it may miss fundamentals on voter history and predilections, and in the last three cycles, there have been a few polling pundits who have used polling aggregates and their presumably deeper understanding of fundamentals to make judgments on who will win the election. Two are the best known are 538.com, a site that used to be part of the New York Times but is now owned by ABC, and Nate Silver's personal assessment, and leading into the election, here were their assessments for the election: Both arrive at their estimates using Monte Carlo simulations, based upon data fed into the system. Note that polls, aggregated polls and poll judgment calls have run into problems in the last decade, some of which may be insurmountable. The first is the advent of smartphones (replacing land lines) and call screening allows callers to not answer some call, and polls have had to struggle with the consequences for sampling bias. The second is that a segment of the population has become tough, if not impossible, to poll, sometimes lying to pollsters, and to the extent that they are more likely to be for one side of the political divide, there will be systematic error in polls that will not average out, and those errors feed into polling judgments. With poll-based forecasts being less reliable and trusted, a vacuum opened up leading into the 2024 elections, and political markets have stepped into the gap. While it has always been possible to bet on elections, either in Las Vegas or through UK-based betting sites like Betfair, they are odd-driven, opaque and restricted. In contrast, Polymarket opened markets on US election outcomes (president, senate, by state, etc.), and through much of 2024, it has given watchers a measure of what investors in that market thought about who would win the election. In the graph below, you can see the Polymarket prices for a "Trump win" and a "Harris win" in the months leading into the election: Note that until July, it was Joe Biden who was the democratic nominee for president, and the only portion of the graph that is relevant is the section starting in late July, when Kamala Harris became the nominee. Mid-year, Polymarket was joined by Kalshi, structured very similarly, with slightly different rules on trading and transactions costs, and that market's assessment of who would win the market is below: Since both markets existed in tandem for the months leading into the election, there were intriguing questions that emerged. The first is that at almost every point in time, in the months that they have co-existed, the prices for a Trump or Harris win on the two pricing platforms were different, with the prices on Kalshi generally running a little lower than on Polymarket for a Trump win. In theory, this looks like an arbitrage opportunity, where you could buy the Trump win on the cheaper market and sell it on the more expensive one, but the transactions costs (1-2% in both markets) would have made them tough to pull off. The second is that within each market, there were a proliferation of contracts covering the same outcome, trading at different prices. For instance, on Polymarket, you could buy a Trump win contract for one price, a a Republican win contract at a slightly higher price, leading into just last week, but that difference could just reflect concerns on mortality. Do the actual results vindicate political markets? At least on this election, the answer is nominally yes, since the political markets attached a higher probability for a decisive victory for Trump in the electoral college than did the poll aggregators or judgments. However, political markets did not expect Trump to win the popular vote, which he may end up doing (some states are still counting), and that can be taken as evidence that markets can be surprised sometimes. In the weeks leading into the election, there were two dimensions on which political markets varied from the polls and aggregators. On the plus side, the political markets were more dynamic, reflecting in real time, responses to events like the debates, interviews and endorsements; Polymarket's odds of a Trump win dropped by almost 10% after the debate. On the minus side, political markets were much more volatile than the polls, with swings driven sometimes by large trades; the Wall Street Journal highlighted one trader who put almost $30 million into the market on the Trump win, pushing up the price. The Wisdom of Crowds That trust in crowd judgments in guiding our actions is not restricted to politics. In an earlier part of this post, I talked about going to the movies, and it is indicative of the times we live in that my movie choice was made, not by reading movie reviews on the newspaper, but by movie ratings on Rotten Tomatoes. Once the movie was done, the restaurant choice I made was determined by Yelp reviews, and without boring you further, you can see this pattern unfold as you think about how you choose the products you buy on Amazon or even the services (plumbing, electrical, landscaping) that you go with, as a consumer. On a less personal and larger scale, the block chains that underlie Bitcoin transactions represent a crowd sourcing of the checking process (performed by institutions like banks conventionally), and you can argue that trusting social media to deliver you information is essentially crowd-sourcing your news. With these examples, you can see one of the dangers of crowd judgments, and that is that in all the crowds described above (Rotten Tomatoes, Yelp, Amazon product reviews and social media), there is no cost to entry, or to offer an opinion, and that can dilute the power of the judgments. In every one of these sites, you can game the system to give high ratings to awful movies and terrible restaurants, and social media news can be filled with distortions. With markets, we introduce an entry fee to those who want to join the crowd in the form of price, and demand more money to amplify those views. In the words of Nassim Taleb, opinionated people with no skin in the game can make outlandish predictions, often with no accountability. If you don't believe me, watch the parade of experts and market gurus on any financial television channel, and notice how they are allowed to conveniently gloss over their own forecasts and predictions from earlier periods. In contrast, no matter what you think about the experience or motivations of traders on a market, they have to put money behind their views. When you use the price in a market as an assessment of the likelihood of an event, which is what you are implicitly doing when you trust Polymarket or Kashi prices as predictors of election winners, you are, in effect, trusting the crowd (albeit a selective one of those who trade on these markets) to be closer to the right outcome than polling experts or opinion leaders. When market price based forecasts are offered as alternatives to expert forecasts, the push back that you get is that experts have a deeper knowledge of what is being predicted. So, why do we trust and attach weight to the prices that investors assess for something? There are three reasons: Information aggregation: One of the almost magical aspects of well-functioning markets is how pieces of information possessed by individual traders about whatever is being traded get aggregated, delivering a composite price that is effectively a reflection of all of the information. Real time adjustments to news: While experts (rightfully) take their time to absorb new information and reflect that information in their assessments, markets do not have the luxury of waiting. Consequently, markets react in real time, often in the moment, to events as they unfold, and studies that look at that reaction find that they often not only beat experts to the punch but deliver better assessments. Law of large numbers: It is true that individual traders in a markets can make mistakes, often big ones, in their assessments of value, and can sometimes also let their preconceptions and biases drive their trading. To the extent that these mistakes and biases can lie on both sides, they will average out, allowing the "right' price to emerge from several wrong judgments. There is also a strand of research that is developing on the forecasting abilities of experts versus amateurs and it is not favorable for the former. Phil Tetlock, co-author of the book on super forecasting, chronicles the dismal record of expert forecasts, and argues that the best forecasts come from foxes (knows many things, but not in depth) and not hedgehogs (with deep expertise in the discipline). To the extent that a market is filled with amateurs, with very different knowledge and skill sets, Tetlock's work can be viewed as being supportive of market-based forecasts. The Madness of Crowds Well before we had Rotten Tomatoes and Twitter were conceived, we had financial markets, and not surprisingly, much of the most interesting research on crowd behavior has come from looking at those markets.. Our experience there is that while markets allow for information aggregation and consensus judgments that are almost magical in their timeliness and assessment quality, they are also capable of making mistakes, sometimes monumental ones. One of my favorite books is Extraordinary Popular Delusions and the Madness of Markets, published in 1841, and it chronicles how market mistakes form and grow, using the South Sea Bubble and the Tulip Bulb Craze as illustrative examples. To those who believe that markets have somehow evolved since then to avoid these mistakes, behavioral finance provides the counter, which is that the behavioral quirks that gave rise to those bubble are still present, and may actually be amplified by technology and large platforms. The falsehood that was born in a pub in the South Sea bubble often looks weeks to work its way into market prices, but the same falsehood on a large social media platform today could affect prices almost instantaneously. Without making this a treatise on behavioral finance, here are some of the problems that can lead markets off course, and make prices poor predictors of outcomes: Noise drowns out information: In finance, we use noise as a term to capture all of the stories and influences that should have no effect on value, but that can still affect prices. While noise exists in even the best-functioning markets, there is enough information in those markets to offset the noise effect, and bring prices back into sync with value. However, if noise is the dominant force in a market, it can drown out information, causing prices to delink from information. Momentum versus Fundamentals: On a related note, it is worth remembering that the strongest force in markets is momentum, where price movements are driven more by price movements in past periods, than by fundamentals. While in a well-functioning market, that momentum will be checked by bargain hunters (if the price is pushed too low) or short sellers (if it is pushed too high), a market where one or the other of these players is either rare or non-existent can see momentum run rampant. It is one reason that I think that markets that restrict short selling, often labeling it as speculation, are creating the condition for market madness. Participant bias: While markets require skin in the game from traders, that requires money, and that biases markets against people with little or no money. In political markets, for instance, it could be argued that the traders on Polymarket and Kalshi represent a subset of the population (younger, better off) that may differ from the voting population. Market Manipulation: The history of financial markets also includes clear cases where markets have been manipulated, to deliver profits to the manipulators. That problem becomes worse in markets with limited liquidity, where big trades can move prices, and where market insiders have access to data that outsiders do not. Illiquidity: All of the problems listed above become greater in a market where liquidity is light, since a large trade, whether motivated by noise, momentum or manipulation, will move prices more. Feedback loop: There are times where market prices can affect the fundamentals, and through them, the value of what is being traded. With publicly traded companies, a higher stock price, for instance, may allow the companies to issue shares at these higher prices, to finance investments and acquisitions. With the political markets, this feedback loop manifested itself in my social media feeds, where I often saw the Polymarket or Kashi charts being used by candidates to convince potential voters that they were winning (to get them to jump on the bandwagon) or losing (to get people to give them money). Political markets are young, attract a subset of participants, and have limited liquidity (though it did improve over the course of the months), and there were clearly times in the weeks leading in to the election, where crowd madness overwhelmed crowd wisdom. On a optimistic note, these markets are not going away, and it is almost certain that there will be more traders in these markets in the next go-around and that some of the frictions will decrease. To "crowd" or not to "crowd" I am convinced that in making our choices as consumers and citizens, we will be facing the choice between market-based assessments and expert assessment on more and more dimensions of our life. Thus, our weather forecasts may no longer come from meteorologists, but from a weather market where weather traders will tell us what tomorrow's temperature will be or how much snow will be delivered by a snow storm. As we face these choices, there will be two camps about whether market prices should be trusted. One, rooted in the wisdom of markets, will push us to accept more crowd-sourcing and crowd-judgments, and the other, building on market madness, will point to all the things that markets can get wrong. While I do believe that, in balance, the wisdom will offset the madness in most markets, there are places where I will stay wary, as a user of market prices. Put simply, rather than view this as an either/or choice, consider using both a market pricing, if available, and a professional assessment. In the context of my discipline, which is valuation, I use both market assessment of country default risk, in the form of sovereign CDS spreads, and sovereign ratings, from the ratings agencies. The latter have more knowledge and expertise, but they are also slow to react to changes on the ground, and I am glad that I have market prices to fill in that gap. If you are planning to trade on these markets, I would hope you will heed my admonition from this post, where I argued that if you are buying or selling something that has no cash flows, you can only trade, not value, it. In the context of political markets, the price that you are paying is a function of probabilities of outcomes and your capacity to make money in the market will come from you being able to assess those probabilities better than the rest of the market. There is another use for these political market securities that you may want to consider. To the extent that you feel emotionally invested in one candidate winning, and you don't have much faith in your probability assessments, you may want to consider buying shares in the other candidate. That way, no matter what the outcome, you will have a partially offsetting benefit; a win for your candidate will make you happy, but you will lose some money on your political market bet, and a loss for your candidate may be emotionally devastating, but you may be able to soothe your pain with a financial windfall. YouTube Video Political Market Links Polymarket Kalshi Book Links Extraordinary Popular Delusions and the Madness of Markets Conclave (Robert Harris)
It is a sign of the times that I spent some time thinking about whether the title of my post would offend some people, as sexist or worse. I briefly considering expanding the title to "Sugar Daddies and Molasses Mommies", but that just sounds awkward, or even replacing the words with something gender neutral, like "Glucose Guardians", but very quickly passed on the idea, deciding to stay with my initial title. After all, I am too old to care about what other people think, and the type of person who would be offended by the title, is probably not someone that I want reading this post in the first place. The message that I was trying to convey, and “sugar daddy” does it better than the alternatives, is that being dependent on an entity to meet your financial needs will impede your capacity to be self sufficient and will undercut accountability. That was the thought that came to mind, as I was writing about the US government's plans to break up big tech, and chronicling how much the big tech companies have struggled, trying to enter new businesses, notwithstanding the capital and brainpower that they have at their disposal. In keeping with my inability to stay focused, that then led me to also think about sovereign wealth funds, an increasingly powerful presence in both private and public equity markets, and then about green energy, a favored destination for impact investors over the last two decades. What do corporate venture capital (CVC), sovereign wealth funds (SWF) and green investing share in common? They all have had almost unimpeded access to capital, from parent companies (with CVC), the government (with SWF) and impact investors (for green investing), and seem to, at least collectively, punch well below their weight, given their size. Corporate Venture Capital Corporate venture capital (CVC) refers to capital invested by established firms, into young companies and start-ups, sometimes in the same business and sometimes in others. The motivations for the practice vary, and the payoff from CVC is debatable, but it is undeniable that CVC is growing as a segment of venture capital, and that it is not only affecting the pricing of the young companies that are targeted, but also altering the economics of venture capital, in the aggregate. Motives To understand why companies turn to investing like venture capitalists, I will bring in my life cycle perspective, with cash available, investment choices and growth potential at each phase: For most young companies, where the free cash flows from existing businesses are negative, because of shaky profitability and large reinvestment needs, investments are likely to be focused on existing businesses, and venture capital will not be on the menu. As companies mature, with business models delivering profits and reinvestment needs declining, it is not surprising the companies look outward, with acquisitions often entering the equation. For those companies that are able to scale up, with growth, and especially so in businesses where there is uncertainty about how the future will unfold (in terms of markets and technologies), venture capital can become a more attractive alternative to both internal investments or acquisitions, because it allows these companies to spread their bets across multiple plays, hoping to hit it big with a few of them. Seen with this perspective, corporate venture capital investments can be framed in one of two ways: Replacement for internal R&D: For some companies, corporate venture capital investments displace internal R&D, designed to generate future products and develop new technologies. This is, again, more likely to happen as companies age, and their internal R&D loses its punch. Arguably, this is the prime rationale for the growing venture capital arms at pharmaceutical companies, with almost $30 billion invested in biopharma ventures just in 2022. As real options: In businesses where there is substantial uncertainty about how product technologies and markets will evolve over time, companies may decide that investing in young businesses with divergent and sometimes competing technologies will yield a higher likelihood of success than investing in just one, either through internal investments or through an acquisition. In effect, this company is creating a portfolio of options in its CVC holdings, and hoping that big payoffs on the options that pay off will cover the costs of the many options that will expire worthless. There are two other reasons why companies may play the venture capital role, and they lead to very different choices in that role: Side benefits to core business(es): A company may make venture capital investments in businesses with the intent of using those businesses to augment core business growth and profitability. Thus, while these investments may not generate payoffs to the company as stand-alone investments, they may still create value, if the side benefits are significant. Stand-alone VC business: In some companies, especially those with slowing core businesses, the corporate venture capital arm can be designed to be a separate business, structured and treated like a stand alone VC business. In this structure, the corporate venture capitalist behaves like regular venture capitalist, with returns measured on finding the right start-ups to invest int and then exiting from their investments, by selling to other venture capitalists, selling the company to an acquirer or taking it public. In summary, corporate venture capital is likely to not only be more diverse, across CVC arms, but even within the same CVC arm, investments can be made with different motives. While corporate venture capital may be viewed as a departure from much of the rest of the investments that a company makes, they are seldom structured as independent entities. Put simply, there are relatively few firms, where there is corporate venture capital arm or division, that is in charge of, and accountable for, CVC investments. A survey of companies with corporate venture capital arms in 2021, for instance, found that less than ten percent are set up as standalone legal entities that resemble institutional venture capital. Many CVC investments are "off the balance sheet", reducing both independence and accountability, but with widely varying capital commitments from the parent company: In some companies, a multi-year capital commitment is made to the CVC, allowing it more freedom to make commitments of its own. In other companies, the commitments are made on annual basis, reducing the autonomy of the CVC in its own investment decisions Finally, there are companies where the capital available to the CVC is residual, reflecting the cash flows to the parent, where individual CVC investments may need corporate approval, reducing independence even further. In sum, no matter how they are structured, CVCs remain tethered to their parent companies, dependent on them for funding, and affecting what they invest in, and how much. Magnitude Corporate venture capital has existed, in one form or the other, for decades, but it has grown to become a larger part of overall venture capital investment, as can be seen in the graph below, where I look at CVC in aggregate dollar value, and as a percent of overall venture capital investment: CVC has grown from less than 25% of overall venture capital investing in 2005 to close to half of all VC investment in 2023. While CVC accounts for a smaller percentage of deals made, it makes up for that by investing in much bigger deals: Corporate venture capital tends to invest in much bigger companies than the conventional venture capital with an average post-deal value of $500 million in 2023, compared to $210 million for conventional VC. To get a measure of how a CVC arm evolves, I took a look at Google Ventures, Alphabet's CVC arm, and one of the largest and most active corporate venture arms in the world. Founded in 2009, and with Alphabet as its only funder, Google Ventures had over $10 billion in invested, in 2024, in more than 400 technology startups, spread across multiple businesses including healthcare, the life sciences and even financial services. Google Ventures has prided itself on using data-driven algorithms to determine what start-ups to invest in, and when to halt a deal, and being manned by engineers, rather than financiers, though it scaled back the practice in 2022. Over its lifetime, Google Ventures has picked some big winners, including iUber, Airbnb and Slack, all of which are now public companies with substantial market capitalization. Not all corporate venture capital forays have happy endings, though, as was the case with SAP, which shut down its corporate venture arm in 2024, seven years after starting it, because of deal setbacks. Performance Going back to the motives for corporations enter the venture capital game, you can broadly categorize CVCs into two groups, broadly based upon the benefits they expected from their investments: Financial: In this category are investments made into venture capital, where the returns come directly from the investment, in the form of cash flows or at the time of exit (in a sale or public offering). Strategic: In this category are venture investments, where the benefits are still financial, but accrue to the parent company in the form of more efficient R&D or as options that pay off, and often more in the long term. A survey of 257 CVC funds in 2024 yielded the following breakdown of where the payoffs are expected: SVB CVC Survey in 2024 Note that only 15% of the surveyed funds are purely financial, with the rest broken up into those that claim either a primarily strategic motive or a hybrid (mix of financial and strategic). It is the mixed objectives of CVC that make it difficult to assess how well it has performed on its investments. Thus, while corporate venture capital collectively generate lower returns for their capital providers than tradition venture capitalist, in their defense, they provide benefits that go beyond the VC returns (in cash flows and exit), to the parent company's bottom line (as higher revenues, lower costs and more efficient innovation). The SVB survey of corporate venture capital provides an interesting picture, contrasting how companies backed by CVC differ from traditional VC-backed companies in terms of exit: Note that fewer CVC-backed companies go out of business, than do VC-based companies, with half the failure rate and more companies advancing to the next round. While this is good news for the funded companies, indicating that CVC funding is more durable and long standing, than traditional VC, it does point to a weakness in the CVC model. VC success comes from finding the right targets, and entering and exiting at the right prices, but it also comes from being ruthless in terms of cutting off companies that do not measure up. To the extent that the data in this table can be generalized to all CVC ventures, that lack of ruthlessness may eat into returns, since weak companies will continue to get funding for longer than they should. There is one final test, albeit a flawed one, to examine whether corporate venture capital adds value to the parent company, at least in the aggregate, by looking at stock price and operating performance of companies with CVC programs. In a 2010 study of 61 firms with CVC arms, the researchers concluded that shareholders of the CVC parent companies react negatively to investments made by the CVC, and also that the reaction was less negative with CVCs that were structured as standalone units. That result clearly is not conclusive proof that CVC is value-destructive, since the optionality or side benefits from CVC are both uncertain and may take a long time to manifest. Sovereign Funds In 1953, Kuwait, seeking to create an investment vehicle for the oil riches that were just starting to emerge, created the very first sovereign wealth fund, i.e., a fund that is funded by the government presumably to protect and advance the interests of its citizens. Since then sovereign wealth funds have multiplied, with a significant percentage still in commodity-rich companies and funded with commodity wealth, but their reach has widened. In the United States, for instance, where the Alaska fund, a funded by the state of Alaska, from oil production, has been the only sovereign fund of any magnitude, both sides of the political divide have started discussing the need for a sovereign fund for the country. Motives Looking across the sovereign fund universe, it is clear that a significant majority of these funds originate in commodity-rich (mostly oil) countries, and that their funding comes from exploiting their oil reserves. Since oil is a finite resource, and reserves will be emptied out over time, it does make sense for countries with commodity riches to set aside some of these richest, in the good years, and to invest those funds for the long term benefit of their citizens. Thus, the first mission that sovereign fund managers have is a conventional one, shared by all active fund managers, which is to deliver returns on their investments that augment and grow the fund. It is this context that they allocate their funding over multiple asset classes, and within each asset class, pick and choose what to invest in. It is true that there are some differences, even on this money management dimension: Sovereign wealth fund managers control a wider array of the portfolio management process than most traditional fund managers. Thus, they often make both the asset allocation decision, as well as the security (equity, bond, real estate project) selection decision, whereas traditional fund managers often have compartmentalized roles, specializing in a specific asset class. Sovereign fund managers also operate under a different set of constraints, with some built into their mission statements, that determine what they can invest in, and how much. Thus, a sovereign fund can be required to invest in some businesses and geographies, and barred from investing in others, whereas conventional fund managers often do not face the same constraints. Sovereign wealth funds face a unique challenge, which is that they have a second mission, which can sometimes be elevated about the fund management mission, which is to serve the national interest, as can be seen in the following examples: Economy building: The Public Investment Fund (PIF), Saudi Arabia's sovereign fund, has been given the mission of delivering on Vision 2030, the Kingdom's ambition plan to wean the Saudi economy away from its dependence on oil. As a consequence, the fund invests a significant proportion of its money in Saudi-based businesses in aviation, defense, entertainment, tourism and sports. Green energy: Given the global angst about climate change, it should come as no surprise that many sovereign funds are required to invest a portion of their portfolios in green energy and renewables, even if those investments do not carry their economic weight. Norges, the largest sovereign wealth fund in the wold, has a renewable energy component of the fund designed to invest in wind and solar infrastructure. Sector strengthening: In some cases, sovereign wealth funds are given the mission of building or strengthening a domestic sector. The China Investment Corporation lists "maximizing return with acceptable risk tolerance" as a core objective, but also lists that its mission includes recapitalizing "domestic financial institutions as a shareholder abiding by relevant laws in order to maintain and increase the value of state-owned financial assets". In fact, much of the talk of a US sovereign fund is driven less by conventional fund objectives, since there are plenty of vehicles that investors (individual and sovereign) can use to try to optimize their returns, given their risk appetites, and more by national priorities that are unfunded or underfunded right now. Magnitude The sovereign fund universe has increased dramatically in the twenty first century. In the graph below, I look at the number of sovereign wealth funds in existence, by year, and the aggregated value of these funds: The number of sovereign wealth funds approached one hundred, at the end of 2023, and they collectively controlled more than $12 trillion in funding at the time. Asia has the largest number of sovereign wealth funds, but the funds from the Gulf/Middle East are among the largest, in terms of funding at their disposal. In fact, you can see their dominance by looking at the list of largest sovereign wealth funds at the start of 2024: In 2024, the largest sovereign wealth fund is the Norges, the Norwegian sovereign wealth fund, which was funded with oil wealth from the North Sea oil reserves decades ago. The Asian entrants on this table include three funds that are from China (including the Hong Kong fund) and two longer standing players from Singapore (GIC and Temasek). While the United States does not have a sovereign fund, the state of Alaska has one, funded again by the state’s oil wealth, with benefits accruing to its state residents; the Alaska Permanent Fund, as it is called, paid a dividend of $1,312 to every Alaska resident (with a residency of at least a year) in 2023, and is expected to pay more than $1,700 a resident in 2024. These funds have wide latitude on investing, and they invest across asset classes - equities, fixed income and alternatives (which include private equity, real estate, infrastructure, hedge funds and commodities) : Source: Invesco (2024) Their investments are in both public and private businesses, as sovereign wealth funds increasingly look for returns in younger companies and businesses that would be targeted by venture capitalists. In terms of structure, there is an extraordinary amount of diversity in how these funds are structured, and who controls the levers and evaluates performance. At one extreme are the Norges and the Singapore-based funds, where transparency is par for the course, and the fund managers enjoy a high degree of independence from governments. At the other extreme, the line between sovereign wealth fund and the government is blurred, opacity (about what the fund is investing in, and how well or badly these investments are doing) is the name of the game and there is little or no accountability. Not surprisingly, the latter group is more vulnerable to political pressure and corruption, with some SWFs becoming slush funds and patronage machines for the politicians that they answer to. Performance The research on active investing suggests that active investing collectively has trouble matching the passive investing returns (from owning index funds), especially after the costs of active investing have been brought into the equation. But how does sovereign wealth fund investing do, relative to passive and other active investing? The answer, at least in the aggregate, is not so well, with equity in the companies targeted by SWFs underperforming the market significantly, with the caveat that performance is much better at transparent SWFs than at opaque ones. Looking at the impact on corporate performance, the results are mixed, with increases in profitability, when the SWF's holdings are less than 2% of outstanding shares, but decreases in profitability and worsening operating performance for larger holdings. In short, if the core mission for sovereign wealth funds is preserving and growing a nation's wealth for its citizens, many of them are falling short, and if it is activism at the investing companies, it is not working. That said, there are outliers, and looking at them may provide us some insight into why sovereign wealth funds under or out perform. While many sovereign funds are opaque on performance evaluation, offering little in public on historical performance relative to benchmarks, Norges provides exhaustive documentation of how their active investing has measured up to passive alternatives. Since the fund is invested in different asset classes, let us focus on just the equity investments made by the fund and the comparison that they provide with a benchmark (admittedly of their creation): As you can see, the fund has outperformed the benchmark, albeit by a very small amount, but given the troubles of active investing, the fact that the alphas are positive is a substantial win. At the other extreme, consider the story of 1MDB, the Malaysian sovereign wealth fund, set up in 2009 with money from an oil joint venture (with PetroSaudi), with the intent of encouraging investment in Malaysia. In the years that followed, hundreds of millions of dollars from the fund was used to fund Hollywood movies and bankroll the lavish lifestyles of connected financiers and politicians, before leading to the jailing of Najib Rezak, Malaysia's prime minister, and a $3.9 billion charge against Goldman Sachs, for the bank's role in the scandal. Green Investing It is undeniable that climate change has moved up the list of global concerns, and if like me, you followed COP28, the climate change conference, this year, or even read news stories about the weather in your part of the world, the need to reduce our carbon footprint does seem urgent, and there are laws, rules and resources that are being directed towards that end. In fact, if investing were measured on the virtue scale, there is perhaps no more virtuous version than green energy investing, and hundreds of billions have been directed towards it. Motives Of the three groups that we look at in this post, green investing's motives should be the simplest to disentangle. It is to push the world away from fossil fuels to alternative energies, but that is where the consensus ends. For some players in this space, reducing the carbon footprint and fighting climate change is the core mission, with returns being a constraint rather than an objective. Thus, for foundations and perhaps even some endowment funds, investing green with as little loss in returns as possible becomes the mission statement. Unfortunately, the bulk of green investors want to have their cake and eat it too. Among impact investors, a prime source of funding for green investors, a significant majority of impact investors (close to 64%) want to have their cake (at or above-market returns, given risk) and eat it too (by making an impact). With equity investors in the green space, this hoped for payoff takes the form of positive alphas, while directing their money to solar, hydro and wind energy investments, and with green loans and green bonds, the higher returns come from being able to earn higher interest on their lending, given default risk. Magnitude of Investment While the speakers at COP28 have lots of legitimate grievances against governments and markets, including the subsidies that fossil fuel companies have received over their lifetime and the laws that enable fossil fuel energy consumption, one grievance that they cannot have is that not enough money has been spent on developing alternative energy, i.e., energy from everything but fossil fuels. Consider the following graph, that reports investments made in billions of US dollars in fossil fuel and alternative energy sources each year. Barring 2015, not only has far more been invested in alternative energy than in fossil fuels, but the difference is widening. In the aggregate, close to $15 trillion has been invested in alternative energy, and other than a very small slice that has gone into nuclear and low-emissions fuels, the rest has gone into green (solar, wind and hydro) energy. The money invested in green energy has come from multiple sources. A small part has come from governments, either directly or through sovereign wealth funds. A significant portion has come from impact investors, a catch-all for investments made by foundations, investment funds, family offices, pension funds and corporate investors in the space, with investments of about $2.5 trillion in 2021, and expected to grow to more than $5 trillion by 2026. Note also that investing in green energy takes both the equity and bond routes, and the green bond market has allowed companies to tap into "lower cost" financing, to facilitate their growth in the alternative energy space. In 2023 alone, $575 billion of green bonds were issued, bringing the size of the green bond market to almost $4 trillion, in the aggregate. Performance - Financial and Carbon Footprint For defenders of green investing, it is good news that that so much money has been directed towards green investing, but that is unfortunately where the good news seems to stop. For the most part, the payoff from green investing has been surprisingly small, on both the financial and the social dimensions, especially given how much money has gone into it. Let's start with the financial payoff from all of the trillions of dollars that have gone towards making the world greener: Business building: When trillions of dollars are invested in a space, you would expect, at some point in time, that this will lead to companies emerging from the space with business models that can deliver sustained profitability and command large market capitalization. In the green investing space, that has not happened (yet). For instance, the 273 publicly traded companies in the alternative energy space (including almost every aspect of that space), in October 2024, had a collective market capitalization of $506 billion, and they reported aggregated revenues of $117 billion in the most recent twelve months. In contrast, just one fossil fuel company, Exxon Mobil alone had a market capitalization of $532 billion, and revenues of $479 billion. Green investing defenders will argue that it will take time for these companies to mature and deliver profitability, but the clock is ticking and the trend lines do not look promising. Investor returns: On the other side of the equation, what type of returns are investors in green energy getting from their investments? The answer will depend on whether you are looking venture capital investors in green energy or public market investors, and also on the time period that you examine. While returns for both groups were robust during portions of the last decade, when investor demand for green investing was high, they have come back to earth, and especially so in the last few years. Here again, your response may be two-fold. The first is that you need patience, for these green energy investments to pay off and deliver profits and returns. The second is that green investing is not about delivering excess returns, but about saving humanity from global warming. I have absolutely no problems with the latter rationale, as long as green funds (both equity and bond) make it clear that they expect to under perform markets, when they seek out capital. In fact, if your response to the financial impact of green investing being unimpressive is that these investment are saving us from global warming, the numbers are not supportive of the virtue thesis. In the graph below, I look at energy consumption, based on source: It is stunning how small an effect the trillions invested in the space have had on where we get our energy, with fossil fuels accounting for about 81.5% of total energy consumption in 2024, about 5% lower than it was twenty years ago . In fact, much of the gains from solar, hydro and wind energy have been offset by a loss in energy product from nuclear energy, the one alternative energy source where almost no money was invested over the period. It is true that there are parts of the world (Latin America and Europe, for instance) where green energy has made significant inroads, but if global warming is an existential crisis, that is small consolation. For those who argue that shifting to green energy takes time, I have two questions. The first is, unless I misheard what climate change advocates are telling me, time is not an ally and we don't have a luxury of moving slowly. The second relates to economics: if it has cost us five trillion dollars (or more) to reduce our dependence on fossil fuels by 5%, will we go bankrupt trying to reduce it by another 35%? There are some who will argue that the money spent on green investing has given rise to innovation and new technologies, but I wonder whether that innovation and those technologies are the ones that we would have invested and developed, without a firehose of capital raining down on green enterprises. There is research starting to percolate through the system that we could have made a much bigger impact on greenhouse emissions by spending our R&D on brown innovations, i.e., innovations that make fossil fuels cleaner-burning and less damaging, than on green innovations, i.e., innovations that explicitly focus on just green energy. More importantly, and as noted earlier, it can be argued that the impact investing definition of alternative energy excluded the one source of energy that has had a track record of making a significant impact on energy consumption, i.e., nuclear energy, and spending a fraction of what was spent on nature's energy sources (solar, wind and hydro) on developing safer ways of delivering nuclear power would have moved the fossil fuel dependence needle by far more. In short, green investing, in the aggregate, has failed in terms of delivering financially (both in terms of business building and delivering returns for investors) and socially (in terms of reducing dependence on fossil fuels).. It is the point that I made in my post on impact investing, where I argued that the prime beneficiaries of the movement have been the consultants, green fund managers, advisors and academics who live in its backwaters. The Sugar Daddy Syndrome Clearly, corporate venture capital, sovereign wealth funds and green investing have very different roots and motives, and have evolved differently, but they do share a common feature. Given how much has been invested in each, they have under delivered, at least collectively, and the vaunted side benefits have been slow to manifest, again with exceptions. I am perhaps overreaching, but here are the reasons as I see them: Assured funding: Each of the three groupings has assured funding, though the degree of assurance and magnitude can vary across individual players. With corporate venture capital, it is the parent company, with sovereign wealth fund, it is the government, and with green investing, it has been impact investors, at least for the last two decades. That assured funding may give them an advantage over their counterparts - VC for CVC, traditional funds for sovereign funds and conventional energy companies for green energy investments- but it does come with a downside. Looking at start-ups and very young companies that manage to make the transition to businesses, one factor that plays a role in focusing attention on building business models is desperation, i.e., the fear that if you do not, you will go out of business. That desperation is lacking in all three groupings highlighted in this post, in many cases. Start-ups and young businesses founded by corporate venture capital may not feel the urgency to create and build business models, if they perceive the capital window at the parent company will stay open. In active money management, a big investing mistake can lead to client flight, but for a sovereign fund, that mistake may quickly be covered by government largesse. Finally, with green investing, one reason that there are so many bad companies and investment funds continue to survive is that they use their virtue at least on the climate change front to attract more capital. Mixed Mission: I noted that for each of the three groups, there is a mixed mission, where, in addition to, and sometimes, instead of, their core missions (start-up to success for CVC, investing alpha for SWF and producing non-fossil-fuel energy at a reasonable price for green investing), they are given other missions. Running any entity, when you have more than one core objective, is always tricky, and it becomes doubly so, when you have two or more objectives, pulling in different directions. Stakeholder distractions: Every entity has multiple stakeholders, and navigating the conflicting interests to deliver success is difficult to do. With the three groupings highlighted in this section, there is at a stakeholder that is the equivalent of a 600-pound gorilla, and what it wants can often overwhelm every other interest. With CVC, that gorilla is the parent company, and the CVC's performance can reflect decisions made at the parent company level that are too big of a handicap to overcome. With sovereign funds, it is the government, and the people who have oversight of the funds, and to the extent that they call the shots, sometimes with other national interests (protect bad banks from failing by investing in them), sometimes with political end games (hire more workers or not fire workers, just ahead of elections) and sometimes for personal reasons (corruption), the SWF can be left with the residue. With green investing, it may be impact investor skews and biases, and governments, that provide the tax benefits and subsidies, pushing companies into technologies and investments that they would not have otherwise. Non-accountability: As you can see, in our discussion of performance for CVCs, sovereign funds and green investing, under performance can always be excused or explained away by either pointing to other mission objectives or arguing that in the long term, success will show up. Thus, a CVC that underperforms a VC will argue that while its corporate ventures did not meet the mark, the side benefits that accrued to the parent company make up for the underperformance. With sovereign funds, it is convenient to point to the other roles - nation building, sector fixing or social safety net - that they play that may excuse the negative alpha. With green investing, the cloak of planet defender comes in handy, whenever the absence of results (either in financial or social terms) is brought up. That said, though, there are outliers in each group that seem to thread the needle of competing missions and interests and deliver successful outcomes. Using some of those successes as guide, I would argue that there are four features that these winners share in common. Independence: With CVCs, we reported that very few are set up as stand alone entities, with control, over funding and investing choices. If you are investing significant amounts of money through a corporate venture capital, it may make sense to not only separate the CVC from the rest of the business, but also to let the individuals that you pick to run the CVC make decisions that are not second guessed. In the context of SWF, one reason that Norges has been able to deliver above-benchmark returns is because its executive board is insulated from government interference. Transparency: In a related point, many CVCs and SWFs are opaque about their working and holdings, with no good business reasons for secrecy. That makes it easier for them to not only hide inefficiencies but almost impossible to assess performance. That opacity is particularly present with the side-missions that these entities are called on to perform - the actions that protect national interests or strengthen financial institutions, for instance, are open for interpretation. At the best performers, though, transparency is more the norm than the exception, and that transparency extends to almost every aspect of how they operate. Separation of motives: I think it was Marc Andreessen who described a house boat, as neither a very good house nor a very good boat. When entities are asked to deliver different missions, intermingling them in decision making will create bad choices. If the Saudi government does want PIF to deliver both solid risk-adjusted returns on its investments and diversify the Saudi economy, it will be better served to separate PIF into two entities - a fund management entity that invests in the best investments it can find and nation-building arm, whose job it is to make the investments or provide the subsidies that work in delivering that mission. Again, at the best performers, there is more of an an attempt to separate core missions from side missions, with clear guardrails on the latter. Accountability: As things stand, it is difficult to hold the entities that make up each of these groups accountable, and the mixed mission is the primary culprit. By separating the missions, accountability becomes easier, since the core mission part of the company can be assessed using the performance metrics of that core mission, and the side mission on how much the money spent advances movement to the social or side goal. That accountability should be followed up with actions, i.e., a greater willingness to shut down corporate venture capital arms that do not deliver and to convert under-performing sovereign wealth funds from active to passive. I went into this post with a hypothesis that corporate venture capital, sovereign wealth funds and green funds/companies underperform their conventional peers - venture capital for CVC, mutual and pension funds for sovereign wealth funds and energy funds/companies for green investors, and that it is assured funding that creates that effect. Having looked at the data, I have rethought my hypothesis, or at least refined it. It is true that, in the aggregate, that the underperformance hypothesis finds backing, with the median player in the CVC, SWF and green investing but there is wide divergence in performance across the players in each group. The very best in each group (CVC, SWF and green investors) match up well to the top players in the peer groups (VCs, actively managed funds and energy companies), with some using their assured funding as a strength to extend the investment time horizons. The key difference, at least as I see it, is that within each of the funded groups, there is not enough pruning of the worst performers, partly because the funders do not or will not demand accountability and partly because the mixed mission statements allow poor performers an excuse for under performance. In contrast, the worst performers in their peer groups are quickly stripped of their funding and drop out of existence. In 2023, an admittedly bad year for venture capital, 38% of active venture capitalists dropped out of deal making. While active funds don’t have as high a drop-out rate, the amount of capital that they invest is sensitive to how they perform in market. That absence of ruthlessness on the funding level for under-performing CVCs and SWFs can trickle down to the companies they fund, with funding lasting much too long, before the plug is pulled. Learning Moments While this post was directed at CVCs, SWFs and green funds, there are broader lessons here for a wider class of investments. Funding always has to have contingencies: When companies, governments or institutions create entities that they commit to fund, that fund commitment has to come with contingencies, where if the entity does not deliver on its promise, the funding will be reduced or even shut off. To the pushback that this will make these entities short term, note that the contingencies that you put in can allow for long time horizons and long term payoffs, but the option of cutting off funding has to be on the table. After all, it is entirely possible that the funder can accomplish what they hoped to, with their under performing entity, with a different pathway. Have a core mission: I sympathize with those who head CVCs and SWFs, when they are faced with a laundry list of what they are expected to deliver, with their funding. Since it is impossible to run an entity, or at least run it well, with multiple missions, you have to prioritize and decide on your core mission. Thus, if you are a sovereign wealth fund, is it your core mission to invest your funding wisely to deliver market-matching or market-beating returns or is it to build a nation’s infrastructure? Social purpose, but with reality checks: In many cases, entities that have a business purpose are also given a social purpose, and while that is understandable, it can give rise to incentives and actions that lead these entities to fail at both. If there is a social purpose component, as there is in green investing and sovereign wealth funds, it has to be made explicit, with clear measures on how much in economic profits the entity is willing to sacrifice to deliver them. In short, claiming that you can deliver good without sacrifice is delusional, and as I have noted in my posts on ESG and sustainability, it is at the heart of the internal inconsistencies and incoherence that bedevil them. Failure can be a strength: In my writing on corporate life cycle, I noted that survival for the sake of survival or growth for the sake of growth will lead to outcomes that make us all worse off. As noted in the last section, the biggest weakness in the three groups is the unwillingness to euthanize underperforming entities, ensuring that good money will be thrown after bad. As a final note, I have mixed feelings about a US sovereign fund, even though there seems to be enthusiasm for creating one, on both sides of the political divide. There are investments, especially in infrastructure, where I see a need for it, but I worry about the political interference and whether this is the most efficient way to deliver that end results that are sought by its backers. 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In a court filing on October 9, 2024, the US Department of Justice (DOJ) let it be known that it was considering a break-up of Alphabet, with the addendum that it would also be pushing for the company to share the data it collects across its multiple platforms with competitors. There is many a slip between the cup and the lip, and it is entirely possible that these are threats designed to extract more concessions from the company, but the break-up talk is a continuation of a debate about the power accumulated by big tech companies, in general, and with Microsoft, Amazon, Apple, Alphabet and Meta, in particular, and what should be done about that power. With politicians, economists and lawyers all in the mix, offering widely divergent solutions, I look at the evolution of anti-trust law in the United States, and whether that law can or should be used to counter big tech. In doing so, I will start with the disclosure that I am not a lawyer, and have no desire to be one, but the problem, in this case, may be that there are too many lawyers involved, and too little business sense. The Law in Spirit and Letter In the latter part of the nineteenth century, as the United States was transitioning from an emerging market to a global economic power, its growth was powered by three industries - steel, railroads and oil - all requiring large investments in infrastructure. In each one of these businesses, powerful men earned their "robber baron" standing by squashing competition and building dominant companies that aspired for pricing power. In oil, it was John D. Rockefeller, who started Standard Oil and built a sprawling empire across the nation, acquiring other players in the still nascent oil business. With Carnegie Steel as his vehicle, Andrew Carnegie took control of the growing steel market, before selling his business to J.P. Morgan, who took it public as US Steel. In railroads, a network of tycoons controlled swathes of the country, with Cornelius Vanderbilt, Jay Gold and Leland Stanford all playing starring roles, as heroes and villains. Along the way, they created the trust structure, organizations of companies which controlled production and prices, effectively monopolizing the businesses . As these companies laid waste to competition, exploited labor and overcharged customers, a political and economic backlash ensued, manifesting in the Sherman Anti-trust Act of 1890 and the election of a Teddy Roosevelt, campaigning as a trust buster. The Sherman Act used the constitutional power of Congress to regulate interstate commerce to authorize the federal government to break up the trusts and "restore competition", with the latter words vaguely defined. While the law outlawed "every contract, combination, or conspiracy in restraint of trade," and any "monopolization, attempted monopolization, or conspiracy or combination to monopolize", the Supreme Court added the constraint that the law only forbade competitive restraints that were "unreasonable". That vagueness initially worked against the government, in its enforcement of the act, with the Supreme Court ruling against it in its attempt to break down the American Sugar Refining Company, in 1896, but the kinks were worked out in the next decade. In 1911, President Taft used the act to break up Standard Oil into multiple oil businesses, and the entrails of that breakup can be found in many of the largest oil companies of today. In 1914, Congress passed the Clayton Act to clarify and augment the Sherman Act, and expanded its reach to cover a whole host of activities that it classified as anti-competitive, including some mergers, predatory pricing and sales ties. It also barred individuals from sitting on boards of competing companies and created the Federal Trade Commission (FTC) as an institution to provide the specifics on what constitutes unfair competition and to work with the Department of Justice, to enforce these rules. In subsequent years, Congress returned to add provisions and modify the act, including the Robinson-Patman Act in 1936, which reinforced the laws against price discrimination, the Celler-Kefauver Act of 1950, which filled in gaps on the merger provisions, and the Hart-Scott-Rodino Act of 1976, which introduced the need for any company planning an acquisition that exceeded a transaction value threshold (reset at regular intervals) to file a pre-merger notification with the Justice Department and to wait at least thirty days before consummating the acquisition. Enforcement Ebbs and Flows The effectiveness of laws at dealing with the problems that they purport to solve depends in large part on how they are enforced. In fact, one reason that the Clayton Act created the Federal Trade Commission in 1914 was to enforce the anti-trust laws, and the FTC states its mission as protecting "the public from deceptive or unfair business practices and from unfair methods of competition through law enforcement, advocacy, research and education." In carrying out this mission, the FTC often relies on the Department of Justice (DOJ), where an antitrust division was created specifically for this purpose, in 1919. Through the history of anti-trust laws in the United States, the enforcement has ebbed and flowed, partly as a result of changing administrations bringing in very different idealogical perspectives on its need, partly in response to Court judgments in its favor or against it, but mostly because of questions about whether the central objective of the laws is to enhance competition or to protect consumers. The divide between enhanced competition and consumers played out in competing viewpoints, with one school, led by Robert Bork, arguing that the original intent of the law is consumer protection, and the other pushing back that the end game of the law is to stop cartels and monopolies, i.e., enhancing competition. That tension continues to underlie much of the debate of the law today, in both political and economic circles, and will come into play if the DOJ pushes ahead trying for a big tech breakup. It is undeniable that for most of the last few decades, the consumer protection argument has resonated more strongly with courts, and has played out as a restraint on what actions the FTC can take, and how far it can go in its enforcement of antitrust law. It is this context that Joe Biden's choice of Lina Khan as the youngest person to head the FTC was viewed a signal of change in focus, since Ms. Khan's most well-read treatise, Amazon's Antitrust Paradox, written while she was still a student at Yale, argued that the company's increasing power was hurting both competitors and consumers. In that paper, she posited that platform-based companies prioritized growth over profits, using their platform size to decimate competition, and that antitrust laws would have to be retooled to rein in these companies. The central part of her argument is that while Amazon’s consumers benefit in the short term, because of lower prices and better service, they would lose out in the long term because less competition leads to less innovation and fewer choices. While her appointment led many to expect a sea change in antitrust enforcement, the effects have been modest, at least in terms of activity: That graph, though, does obscure the fact that the government has been more aggressive about challenging high profile mergers, and publicly proclaiming its intent to do so, in others. The results have been mixed, with wins in a few cases coming with losses in several others, with the failure to stop Microsoft's acquisition of Activision representing one of it s highest profile losses. In short, while Ms. Khan's argument for use of antitrust laws to restrain platforms may have found a receptive audience among some legal thinkers and politicians, it has not won over the courts (at least as of now). The Remedies: Sticks and Stones! No matter where you fall on the consumer versus competitor protection debate, the remedies available to the government fall into three groups, ranging from its power to stop (require) activity that it believes will stymie (advance) competition to breaking up companies, with the possibility, albeit rarely used, of allowing a company to establish monopoly power, but with pricing power restraints. 1. Operating restraints and changes The anti-trust laws give the government the power to affect how a company operates by stopping it from acting (by acquiring another company, introducing a new product or entering a new market) or changing its behavior (in terms of pricing it products and operating its business), in the interests of increased competitiveness. In doing so, though, the courts require the government to make the case that the actions that it is stopping or the behavior it is altering are unreasonable and that it meets the "rule-of-reason" threshold, i.e., that there are anticompetitive effects that exceed any pro-competitive effects. a. Merger Challenges Corporate mergers in the United States, where the transaction value exceeded $111.3 million in 2023, required the acquiring company to file a pre-merger notification with the Justice department, with consummation of the merger happening only after approval. In its most recent update to requirements on pre-merger notifications, the DOJ expanded its information disclosure requirements to include transaction-related documents from deal teams and more complete information about both the products and services offered by the companies, as well as about corporate governance. As we noted in the last section, the degree to which the government uses it power to challenge mergers has waxed and waned over time, and even if challenged, the last word rests with the courts. In a report that it is required to file under the Hart-Scott-Rodino Act for the 2023 fiscal year, the DOJ listed out the number of merger challenges for the year (16), breaking them down into wins (1), consent agreements (4), ongoing litigation (1) and abandonments/restructured complaints (10). The report also lists out the industries that were targeted the most, in terms of merger challenges: Hart-Scott-Rodino Annual Report for 2023 (DOJ) Again, note that notwithstanding Ms. Khan's high profile thesis on the need for antitrust enforcement against technology companies, the bulk of the challenges have been directed at more traditional businesses. b. Operating Changes In some settlements, the government extracts concessions from a targeted company that it believes will improve the competitive standing of the business. These can range the spectrum, and I will use some of the 2023 settlements to illustrate: Forced divestitures: As part of a settlement allowing a proposed merger of Vistra Corporation to acquire nuclear plants owned by Energy Harbor Corporation, where the FTC raised concerns about less competition and higher energy prices for consumers, Vistra agreed to divest its power plant in Ohio. In its challenge of Intercontinental Exchange's acquisition of Black Knight, it required Blue Knight to divest some of its businesses, as a condition for the merger to go through. Product bundling/Pricing: As a condition for allowing Amgen to move forward on its acquisition of Horizon Therapeutics, where the FTC feared that Amgen would use its large drug portfolio to pressure pharmacies to push Horizon's two monopoly products, the FTC secured a consent order where Amgen agreed not to condition any of its product pricing or rebates on whether Horizon drugs were prescribed. Corporate governance: In EQT's acquisition of Quantum, the FTC's concern was that as these companies were direct competitors, giving EQT a seat on the board and a large shareholding in Quantum would reduce competition. Consequently, EQT was forced to divest its EQT shares and was prohibited from having a board seat. In most of these cases, the government used the threat of more extreme punishment to extract concessions from the targeted companies. c. Pricing Oversight If it is price fixing by a company that has drawn the attention of the antitrust enforcers, it is possible that the remedies sought will reflect changes in the way a company prices its products and services. In 1996, Archer Daniels Midland (ADM) pleaded guilty to fixing prices for Lysine, an animal feed, in collaboration with Japanese and Korean companies. The company, in addition to paying a large fine and having top executives face jail time, was also required to change its pricing processes. In 2024, the FTC published a warning that the use of algorithms by multiple competitors in the same business, to set prices, can violate antitrust laws, and sued RealPage, a property management software, for allegedly allowing landlords to use its algorithms to drive up rental prices. As AI makes algorithmic pricing more of a norm in other businesses, the FTC will undoubtedly be challenging more businesses on pricing practices. 2. Break ups The most extreme action that the DOJ can take against a company in response to what it views as anti-competitive behavior is to break up the company. Since their effects on the company in question are so wrenching, they are rarely pursued and even more rarely court-approved, but when they do occur, they are memorable. Here are three that stand out: The Standard Oil break up, in 1911, was not just the first big break up in history, but given that it targeted what was then one of the largest companies in the United States, it had major consequences. At the time of the breakup, Standard Oil effectively controlled the entire oil business and it was forced to break itself up into thirty four companies: The eight major companies that emerged from that breakup have morphed over time, and remain dominant players in the oil business, albeit in modified form. The other big breakup of the twentieth century happened closer to the end, when AT&T, then the monopoly phone company in the United States, was broken up into a long distance company (AT&T) and seven baby Bells, based upon geography: A few decades later, the business has not only changed dramatically, but it has reconsolidated itself into four ventures, with AT&T and Verizon remaining the biggest players. The third breakup, albeit one that did not go through, targeted Microsoft in 2000, where the DOJ sought to break up the company, separating its operating system (Windows) from its application software and browsing businesses (Office and Internet Explorer). The courts initially found in the government's favor, but that ruling was subsequently set aside. Eventually, the company settled, agreeing to share some of its application programming interface with third-party company, but avoided major restructuring. While each of these breakup (including the potential Microsoft one), got significant attention at the time that they happened, the net effects on competition, consumers and the companies themselves are still being debated, and we will return to examine the trade offs in the next section. 3. Regulated Monopolies The phone business was still in its nascency, when the Willis Graham Act was passed in 1921, arguing that "(t)here are monopolies which ought to exist in the interest of economy and good service in the public welfare, monopolies which must be promoted instead of being forbidden. The telephone business is one of these. Legitimate consolidation will promote economy. It will promote service. It is foolish to talk about competition in the transmission of intelligence by telephone. It is silly to believe that there can be real competition either in service or in charges… The thing that the American Congress ought to do is to.. regulate those monopolies so as to get reasonable prices and good service for the people…" That act allowed AT&T, then the leading phone company in the United States, to acquire its mostly troubled competitors to create a monopoly, with a catch. That catch was that the company's pricing power would be regulated to deliver a reasonable rate of return for its investors, thus creating the basis for regulated monopolies. The notion of a natural monopoly was not restricted to just telecommunications, and was used for other utilities, such as water and power, with the only difference being that most of the companies offering those utilities obtained local monopolies rather than national ones. Arguably, the decision delivered benefits for customers, as the services were extended to almost every part o the country, albeit at the cost of innovation. As a side benefit, these regulated monopolies, protected from competition, had the capacity use their surplus funds to support activities that sometimes generated societal benefits, that they would not have in a competitive marketplace. With AT&T, that was the case with with Bell Labs, AT&T's in-house research laboratories, where some of the greatest inventions of the twentieth century were made. The End Game I mentioned at the start of this post that I am not a lawyer, and I understand that antitrust is full of shades of gray, where absolutism can lead to poor outcomes. Thus, I do get Robert Bork's point that the ultimate endgame in antitrust law is not promoting competition, for the sake of competition, but only if delivers net benefits to consumers. At the same time, I don't think we can dismiss Lina Khan's arguments that large tech companies, using the networking benefits and access to data from their immense platforms, can obtain monopolistic power that may work against consumer interests in the long term, not only by stymying innovation, but also potential increasing prices for consumers down the road, once they reach dominance. At the risk of adding to an already complex trade off, I believe that three other factors have to come into play in assessing the right action forward: Business economics: The notion that increased competition increases innovation and delivers more consumer surplus is deeply set, at least as taught in basic economics courses, but there are businesses where that is not true. In these businesses, the business may be more efficiently run and customers better served, with fewer competitors, rather than more, and to illustrate, consider two examples. The first is the airline business, an absolute mess, where none of the stakeholders (investors, employees, customers, managers or regulators) feels well served, as we lurch from boom to bust. Forty seven years after the business was deregulated, a strong case can be made that the business will be better served with consolidation and allowing more of the weakest players to fail. It is worth noting that the most activity in the Lina Khan DOJ stint have come against airlines (JetBlue and Spirit, a withdrawn challenge to Alaska and Hawaiian), with consumer protection as the rationale, but with no serious assessment of business viability. The second is the streaming business, where Netflix has broken the entertainment business, but it has not been replaced with a viable business model. In fact, as you sort through a dozen streaming choices, it is quite clear that most of these services cannot subsist on their own, with the only pathway to viable business models being a consolidation into three or four streaming services. Forcing competition in businesses where consolidation is the better path to efficiency will create more unstable businesses, more unhealthy competitors and more unhappy customers, i.e., there will be no winners. Investors: Implicit in antitrust law and enforcement is the belief that investors in the errant companies are the beneficiaries of anti-competitive actions, but is that true? In the case of trusts, it was quite clear that by clearing the competition and exploiting their monopoly power, investors in the trusts benefited. There are anticompetitive actions, however, where it can be argued that investors see little in benefits from the actions, in the short or the long term, even though managers may rationalize them as beneficial. Thus, if the argument is that a company is using a cash cow business to subsidize its entry into other businesses, investors and regulators may be on the same side on the question of shutting down that subsidization. Ultimately, anti-trust actions are more likely to find investors as allies, if the company being targeted is mistrusted by investors and has a track record of wasting money on long shots. Economy and Markets: It is also worth emphasizing that as government regulators, the antitrust enforcers have to consider how their actions against companies, on antitrust grounds, play out in the nation's economy and its markets. If, by allowing a company or companies to reach a dominant position in the market, you are increasing their competitive advantages against foreign competitors or adding to the aggregate payoff to investing in stocks in markets, should you put those gains at risk by handicapping those companies? It is worth remembering that the Chinese government decided to crack down on its tech giants (Alibaba, Tencent, JD) in 2019, motivated more by control than by any consumer or competitive interests, and in the process not only set them back in the global markets by a significant amount, but hurt the Chinese economy and markets. If you bring these all into the mix, you will be making the work of antitrust enforcers even more difficult, but you will be considering the effects of your actions more fully: If your job as an antitrust enforcer is to balance competing interests, and do what is right only if there is a net plus to your action, you should be considering the effects of antitrust activity on all four dimensions. That said, if you have blinders on, and view only one of these dimensions (consumers, competition, company or the economy) as critical, it is entirely possible that the actions you take can have net negative consequences, in sum. Using this framework to assess the AT&T break up in 1981, the break up into seven regional phone companies and a long distance one was initially praised as an action that would promote innovation and new thinking, but history suggests otherwise. The regional phone companies continued to behave like the old Ma Bell, investing little in new technologies, and continuing with the high debt and high dividend policies of the original. Much of the innovation in telecommunications came from outsiders entering the business, and the business itself has reconsolidated suggesting that the economics cannot support a dozen or more players. And just as a bonus, Bell Labs was renamed Lucent Technologies, and after an initial burst of enthusiasm about promise and potential, sank under its contradictions. The Big Tech Dilemma This post was precipitated by the Justice department’s targeting of Alphabet, with threats of a break up and requiring the company to share its data. While neither threat has been made explicit, it is worthwhile thinking about how the big tech companies measure on the competitiveness scale, and whether antitrust law can or should be used to cut them down to size. The challenge, as we will see, is that we all agree that big tech has become perhaps too big, but the question of how it got that big has to be answered before we respond to the bigness. The Rise of Big Tech Looking at the DOJ's arguments for breaking up Alphabet, it is clear that the same arguments can be used against some of the other big tech companies. In this section, we will look at Alphabet, Amazon, Apple, Microsoft and Meta (bundled together as the Fearsome Five), all of which have been rumored, at times, to be in the crosshairs of antitrust enforcers, and the reason for their targeting, which is that they are all big, perhaps even "too big", and that can be backed up with multiple metrics: a. Market Capitalization: If the companies that we have listed look like they belong together, it is because they were bundled as the FANGAM stocks in the last decade and as part of the Mag Seven in this one. In each case, that bundling was used to illustrate how dependent the US equity markets have become on just a few stocks, to deliver overall equity returns. In the graph below, we look at the rise of these companies, in terms of market capitalization, since 2010, and how much of the aggregated market cap at all US stocks has come from just these companies: As you can see, these five companies, in the aggregate, increased their dollar market capitalization from $716 billion at the end of 2009 billion to $12.1 trillion on October 16, 2024, accounting for 23.16% of the increase in market capitalization across all US equities over that period. On October 16, 2024, these five companies accounted for 20.22% of the market capitalization of all 6132 US equities, and in sum, they had a market capitalization that was greater than that of any other equity market in the world. b. Revenues and Earnings: The rise in market capitalization did not just come from vibe or momentum shifts and was backed up increases in revenues and income over that period that were truly extraordinary, given the scale of these companies: These companies increased revenues 18.8% a year between 2009 and 2024, while preserving enviable profit margins - gross, operating and net margins stayed relatively stable. In sum, these companies have delivered a combination of revenue growth and operating profitability that is unmatched, given the size of these companies, in history. c. Day-to-day life: There is a final component on which you can measure how big these companies have become, and that is to look at how much of our time and lives is spent on one or more of their platforms. In a New York Times article from 2020, the writer talked about trying to live without big tech for six weeks, and how difficult she found the consequences to be. During the same year, I chronicled in a post how much time I spent each day on the platforms on one or more of the big tech companies, essentially concluding that I was in their grip for all but fifteen minutes of the day. As a thought experiment, consider what your day at work or at home will look like today, if all five of the Fearsome Five decided to make you persona non grata. Mine would be a grind, with this post not being written (it is on a Google Blog), the graphs not showing up (they are in Microsoft Excel) and my computer not responding (it is a Mac). In short, I don't there is any debate that the big tech companies have become big on every dimension, and become central players not just in the economy and markets, but in our personal lives. It is therefore no surprise that when Lina Khan and others argue that these companies have become too big, and need to be restrained, they find a receptive audience. Pathways to Bigness While, for some, bigness alone is a sin that needs to be punished, the pathways that these companies took to get to where they are now needs to be examined for a simple reason. If those pathways were cleared by legitimate business actions and choices, it would not only be unfair to punish them for their success in foiling competitors and establishing dominance, but it would also make the legal challenge of using antitrust laws to restrain them much more daunting. In this section, we will look at what these companies did (and are doing) that explains their success. Core Business Dominance: Looking at the fearsome five (Amazon, Apple, Meta, Alphabet and Microsoft), each one, with the possible exception of Microsoft, has a core business in which it dominates, driving the bulk of it revenues, with Microsoft perhaps being the exception. For Alphabet and Meta, that core business is online advertising, with Apple, it is the iPhone, and Amazon's revenue base is in the retail business. Microsoft's dependence on its software business has waned over the last decade, and while Windows and Office continuing to deliver as cash cows, the company has increasingly become a cloud and business services company. Shaky Side Businesses (with a cloud exception): Largely funded by cashflows from their core businesses, the big tech companies have tried to enter new businesses, mostly with little to show for their investments. Alphabet has been most open about its ambitions to be in multiple businesses and its renaming was largely a signal of that intent. Amazon's ambitions to be a disruption machine have been widely documented, with forays into logistics, entertainment and even health care. Apple has been more restrained, but it too has tried its hand at entertainment and other businesses. Meta, after facing market backlash for its badly framed entry into the Metaverse, has retooled itself and is trying for success in AI and virtual reality. For the most part, these side businesses have been cash drains, and added little in value, with one exception. For three of these companies, Amazon, Alphabet and Microsoft, the cloud business has become not only a large part of their revenue base, but also an even bigger contributor to their profitability. With Apple, the services business is offering promise in terms of growth and is a gold mine when it comes to profitability, but it draws much of its value from the iPhone franchise. Consumer subsidies: These companies have also created subsidy mechanisms for consumers, offering them products and services that are "free" or "bargains", at least on the surface. Amazon Prime remains one of the best deals in the world for consumers, since for an annual fee of $139, you get free shipping, entertainment and a host of other services. In fact, Amazon makes explicit the cost of the shipping subsidy in its annual reports each year, and it has spent tens of billion each year for the last decade, supporting that service. Alphabet offers a whole range of products, from Google Docs to Google maps, at no explicit cost, and there are hundreds of millions that use WhatsApp around the world, with no monthly charges or fees. Apple and Microsoft, befitting their standing as the elder statesmen in this group, have been more stingy about providing free add ons, but they too have sweeteners that they offer, usually in exchange for data from users. The question then becomes whether any of this is "unfair", and the answer is debatable. Listening to those most critical of these companies, there are five arguments that I have heard to back up the "uneven playing field" argument: Subsidize their product offerings: One of the critiques of tech companies is that they use the massive profits they generate from their businesses, core and cloud, to subsidize their product offerings to customers. By doing so, critics argue, they make it more difficult, if not impossible for competitors, to succeed in these subsidized businesses. That is probably true, but cross product subsidization, by itself, is neither uncommon, nor illegal, and consumers are the beneficiaries. Networking benefits: Most of these companies have large platforms, and in the businesses that they operate in, that can work in their favor. In online advertising, Alphabet and Meta have a significant advantage over competitors, because advertisers want to go where people gather, and they are more likely to find that on larger versus smaller platforms. That said, those networking benefits are inherent in online advertising, and punishing the companies that were able to climb the competitive ladder most competently does not seem fair. Use of private data: When users spend their time on the tech company platforms, they are providing data to these companies that can be used to their benefit. Staying with the online advertising giants, Google and Meta, is clear that the information that they collect from user interactions on their platform is being used to target advertising better, making them an even more attractive destination for advertisers. While conceding these points, it is worth noting that advertisers should have no complaints about better targeted ads, users share private data voluntarily, in return for conveniences, leaving competitors again as the only complainants. Squashing competing technologies: When your platforms become ubiquitous, your competitors might need your permission to play on these platforms, and the big tech companies often make it either more difficult to play or claim a large chunk of revenues. Apple, for instance, has faced pushback because it charges a 30% fee for third-party apps that go through its app platform, and Google has also received criticism for restricting third party app stores on Google play and Android. Here, the argument can be made that in addition to competitors being hurt, consumers are being denied choice and paying higher prices for third party offerings. Not paying fair price for content: Many of the big tech platforms allow users to access content for free, with the content developers feeling shortchanged. The big tech companies benefit from this content access, because that access increases platform usage and their revenues (from advertising, device sales etc.), but in a fair system, they should be sharing this revenue with the content developers and providers. It is at the heart of the tussle that is ongoing between media companies (newspapers, magazines) and the big tech companies, and while the former are becoming more savvy, they are operating at a disadvantage. I am sure that all of these issues will be litigated, but I do think that governments (and antitrust enforcers) are on far stronger ground, on the last two, than on the first three. More generally, if you were to look big tech sins, there are two general conclusions: Hurt competitors, subsidize consumers: As you look at the critique of big tech, it is clear that the damage from big tech company behavior has been felt mostly by competitors. In fact, consumers for the most part have benefited from the subsidies that they have received, and if they are aggrieved about the use of the data that they have shared with the companies, it is unclear how much they have been hurt by that sharing. Current versus Prospective sins: Extending the first point, even the most severe critics of big tech argue that the costs of allowing them to dominate will be in the future, Lina Khan's criticism of Amazon is that while customers benefit right now from Amazon Prime and other freebies, there will be costs they bear in the future that will outweigh the benefits. In particular, she argues that there will be less choice and innovation, because of Amazon's dominance, and that Amazon will eventually become powerful enough to raise prices, and consumers will have nowhere to go. The problem that Ms. Khan and others in her camp will face is that there is nothing in the company's behavior currently that would lead us to extrapolate to those dire endings. Ultimately, anti-trust actions are as much about politics as they are about economics, and they work only if they carry public approval. On economic grounds, that is why pushing strong anti-trust actions against big tech will be a much more difficult sell than against other dominant businesses in the past. After all, how do you convince customers that they paying more for Amazon Prime and being charged for Google Maps will make them better off, because there may be more innovation and choice in the futures with more competition? The Choices The DOJ court filing suggests that the die has been cast, and that Alphabet will be the target of the anti-trust enforcers in the near future, with success or failure in that endeavor perhaps resulting in expanded action against the other big tech companies. Using the framework from the last section in assessing the costs and benefits to consumers, competitors, investors and the economy, we can evaluate the choices. 1. Break up Can the government break up Alphabet, just like it did AT&T and Standard Oil, in the last century? It can push for it, but to understand why it will be difficult, and even if plausible, unwise, here are some considerations: While you can think of the multiple platforms that Alphabet operates as separate, the truth is that the core business is advertising, and whether you are on the Google search box, YouTube or on Android, that business derives its value from keeping users in the Google ecosystem, rather than on independent platforms. With Facebook, that problem is magnified, since Facebook, Instagram and WhatsApp are all part of the same ecosystem, with the end game keeping you in it. In short, the platforms, separated, would both be unable to survive as stand alone businesses as well as less attractive destinations for users. There is an added reason why breaking either Alphabet or Facebook into individual platforms makes no economic sense. Online advertising is a business with networking benefits, and any solution that pushes you away from consolidation, may create more competition, but will worsen business efficiency and health. In fact, assuming that you were able to break both Alphabet and Facebook into individual platforms, it is not clear to me who will benefit. Consumers will no longer have access to their subsidized products, online advertising will be less targeted and effective for advertisers and even the competitors who may be helped in the near term will find those benefits fade quickly. As we noted in the last section, the big tech companies have generally not been able to deliver value in their side ventures, with the exception of their cloud businesses, for Alphabet, Amazon and Microsoft, and the services business. You can demand that Alphabet be forced to divest itself of all of it non-ad related bets, but very few of these businesses can stand alone. It is true that the cloud businesses have the capacity to stand alone, but what is the argument that you would use for forcing divestiture? After all, in the three companies that have significant cloud businesses - Alphabet, Microsoft and Amazon, their success in the cloud had little or nothing to do with core business domination and divestitures make it less likely that consumers gets subsidized products, which will make them worse off. In addition, divesting these businesses will do nothing to break the dominance that these companies have in their core business, since that dominance comes from networking benefits and private data. In fact, the only company where an argument can be made for a break up is Apple, where the services business draws its value from the Apple stranglehold on the smartphone business. Summarizing, breaking up any of the big tech companies risks the worst of all outcomes. It will make the companies (and their investors) worse off, but not by as much as critics think, but it will also have negative effects that ripple across the economy and markets, while making the businesses that they operate in less efficient. Competitors will derive short term benefits from the breakup, but those benefits are unlikely to last, if the business economics still point towards consolidation. Finally, consumers will be left off worse off, in the short term, with only promises of a better tomorrow filling the void. 2. Regulated Monopoly The second pathway that has been suggested is that the government big tech companies as regulated utilities, just as they did phone, power and other utility companies in the last century. While that would give the government power over how these companies price products and services, and make them less profitable, the flaws in the argument are large and potentially fatal: The regulated monopolies of the last century agreed to the pricing restriction quid quo pro because the government gave them monopoly power in the first place. With tech companies, what exactly would the government be offering these companies in return for the loss of pricing power? With Alphabet and Meta, the online advertising market is not the government's to give away, and with smartphone (Apple) and online retail (Amazon), it becomes an even bigger reach. If, in fact, the government did get control of pricing power at these companies, who would be the beneficiaries? With online ads, the benefits would flow to the advertisers, a transfer of wealth from one set of companies (the Big Tech advertising companies) to another set of businesses (the many companies that advertise on the tech platforms), and that is neither fair not equitable. If the end game is innovation, and with technology, it is the lubricant for success, creating regulated monopolies and requiring them to earn their cost of capital will not only destroy incentives to innovate, but leave these companies exposed to disruptors from other markets. In short, there is no pathway that works to make any of the big tech companies look like Ma Bell, and even if that pathway existed, how would that benefit consumers, markets or the economy? 3. Targeted changes Given how much of a reach it would be to break up the big tech companies or bring them under the regulated monopoly umbrella, the pathway, if the government is intent on sending a signal will take the form of constraints on and changes to operating practices. I will start with a list of changes, where I think that the government has a better chance of prevailing, because the laws and public opinion will be on their side: Platform access: If you own a platform where users congregate, you cannot make the roadblocks to third parties being on the platform so onerous that they are put at an almost insurmountable disadvantage. I think that Apple and Alphabet will be pushed to make their platforms more accessible (technically and economically) than they are right now. Paying for content: As AI looms larger, the fight over content ownership will get more intense, since AI can not only be a monstrously large consumer of content, but can do so with little heed to where the content comes from, or who owns it. Content owners and developed may need an assist from the government, when they fight to reclaim the content that belongs to them. Customer and User Recourse: The power dynamics when you use a tech platform are imbalanced, and as a user or customer, you often have no power against the company operating the platform, if it chooses to act against you. As someone who has kept my blog on Google Blogger and my videos on YouTube, there is almost nothing I can do if Alphabet decides to shut them both down, other than appeal to the company and hope to get a fair hearing. Governments may push more formal appeals processes, with independent arbiters, to provide for more balance. There are three other changes, where the government is less likely to succeed, and deservedly so: Share data with competitors: It is possible that the government will try to get tech companies to share the data they collect, but I believe that this is neither fair nor a competitive plus. While having the data gives them an advantage over their competitors, that can be said about competitive advantages in many other businesses, and companies in those businesses are not asked to do the equivalent. Does Coca Cola have to share its syrup makeup with competitors because it has the most valuable brand name in the beverage business? Should Novo Nordisk be asked to share its patent rights for Ozempic and Wegovy with other pharmaceutical companies, because having these rights gives it a leg up in the weight loss business? If your answer is no, why would you use a different set of rules for big tech companies. Of course, if your answer is yes, your problem is not with big tech but with capitalism, and that is an argument for a different time and setting. No cross subsidization: It is also possible that the government will take a stand on cross business subsidies, arguing that the money that big tech companies make in one business should not be used to establish advantages in other markets. The problem is that cross subsidization is part of almost every large company, where successful, cash-rich portions of the company subsidize cash-poor portions, perhaps with growth potential. Those subsidies can sometimes hurt shareholders of the company, but it is not the DOJ's job to provide them with protection. In fact, the big tech companies have not been immune from investor backlash, as Meta found out, when it pushed its Metaverse investing plans forward with no clear pathway to monetization. Device Compatability: Big tech companies are often criticized for making it difficult for other company devices to play on their platforms. Thus, the Apple platform works much better with Apple devices (iPhones, iPads and Mac computers, Apple iPods) than with Android devices. Much as this may frustrate us, as consumers, no company should be obligated to make it easier for competitors to take business away, and government attempts to suggest otherwise will be heavy handed and ineffective. 4. Do nothing There is a final option, and it will not be appealing to many anti-trust enforcer who came into their professions wanting to push for change. That is to do nothing! That sounds defeatist, but at least in technology, it may be the best choice, given the following: Tech life cycles are short: As many of you may be aware, I believe that companies, like human beings, go through a life cycle, evolving from start-ups (baby) to mature (middle age) to decline (old age). That said, there is also evidence that tech companies age in dog years, scaling up much faster, not lasting at the top as long and declining much more quickly than non-tech companies. That, in turn, reduces the need for governments to intervene on behalf of competitors or consumers, since tech companies that look unassailable and dominant today can quickly find themselves under threat in a few years. The Innovation Trade off: As an extension of the first point, if innovation costs money, and life cycles are short, companies have to be allowed to make money during their brief stints at the top, to justify innovation. In short, if you make the lucrative years for a tech company less so, by taking away pricing power and capping profitability, it will reduce the incentive to start and grow new technology companies. I don't think it is coincidence that the EU, where rule makers take a dim view of excess profits and market power, has no great tech companies. Disruption is always imminent: To the extent that big tech companies are tempted to play it safe, cutting back on innovation and using their market power to increase prices on customers, i.e., the Lina Khan doomsday scenario, they expose themselves to disruption far more than manufacturing or consumer product companies do. Blackberry's failure to adapt left them exposed to the smartphone disruption, and Yahoo! lost its search engine dominance to Google in the blink of an eye. I would wager that the big tech companies are acutely aware of that threat, and I don't blame them for creating as safety buffers. You may have guessed already, but I do believe that doing nothing is, in fact, the most sensible option, with big tech companies. Are there risks in adopting this path? Absolutely! The big tech companies may have found ways to extend life cycles and they may buy out disruptive innovation, just to squash it, and we may all be worse off, as a consequence. I have seen no evidence of any of that behavior so far, but that fear remains, and I will remain vigilant. Conclusion I do not see eye to eye with Lina Khan, but I will start with the presumption that she has good intentions and that her argument is deeply thought through. My concerns with her big tech views are two fold. The first is that she is a lawyer, and law schools around the world do an awful job on teaching their graduates about business, which is one reason that laws tend to be one-size-fits-all. Just to illustrate, competition is good in some businesses, but consolidation works in others, and a law or lawyer that does not discriminate between the two will do more damage than good. The second is that she is a true believer, and if you start with the view that big tech companies are evil, you will undoubtedly find good reasons to cut them down to size. I do recognize that there are non-economic considerations at play, and that you may fear the effect that big tech platforms are having on our politics and social discourse. I share that concern, but I am not sure that there is an economic solution to that problem. If you think that breaking up Google and Meta will lead to more polite discourse on social media and a return to the cultural norms of yesteryear, you are being naive, since the problem lies not in Twitter, Facebook or Reddit, but in ourselves insofar as participating on social media seem to bring out the worst in us. I am afraid that we have opened Pandora's box, and there is no shutting it now! YouTube Video Data/Spreadsheet Links Fearsome Five: Market Capitalization from 2009 to 2024 Fearsome Five: Operating Details from 2009 to 2024 Annual Financial Filings (most recent) Alphabet 10K (Year ended December 2023) Meta 10K for 2023 (Year ended December 2023) Amazon 10K for 2023 (Year ended December 2023) Apple 10K for 2023 (Year ended September 2023) Microsoft 10K (Year ended June 2024)
I have spent the last week reading "Shoe Dog", Phil Knight's memoir of how a runner on the Oregon University track team built one of the great shoe companies in the world, in Nike. In addition to its entertainment value, and it is a fun book to read, I read it for two storylines. The first is the time, effort and grit that it took to build a business, in a world where risk capital was more difficult to access than it has been in this century, and in a business where scaling up posed significant challenges. The second is the building of a brand name, with a mix of happy accidents (from the naming of the company to the creation of the swoosh as the company's symbol to its choice of slogan), good timing and great merchandising all playing a role in creating one of the great brand names in apparel and footwear. The latter assessment led a more general consideration of what constitutes a brand name, what makes a brand name valuable and what causes brand name values to deplete and disappear. Of course, since my attention was drawn to Nike in the first place, because of a change at the top the company and talk of brand name malaise, I tried my hand at valuing Nike in 2024, along the way. Brand Name - What is it? The broadest definition of a brand name is that it is recognized (by employees, consumers and the market) and remembered, either because of familiarity (because of brand name longevity) or association (with advertising or a celebrity). That definition, though, is not particularly useful since remembering or recognizing a brand, by itself, tells you nothing about its value. After all, almost everyone has heard or recognizes AT&T as a brand/corporate name, but as someone who is a cell service and internet customer of AT&T, I can assure you that neither of those choices were driven by brand name. The essence of brand name value is that the recognition or remembrance of a brand name changes how people behave in its presence. With customers, brand name recognition can manifest itself in buying choices (affecting revenues and revenue growth) or willingness to pay a higher price (higher profit margins). With capital providers, it may allow for lower funding costs, with equity investors pricing equity higher and lenders accepting lower interest rates and/or fewer lending covenants. For the moment, this may seem abstract and subjective, but in the next section, we will flesh out brand name effects on operating metrics and value more explicitly. Corporate, Product and Personal Brand Names Brand names can attach to entire companies, to particular products or brands, or even to personnel and people. With a company like Coca Cola, it is the corporate brand name that has the most power, but the soft drink beverages marketed by the company (Coca Cola, Fanta, Sprite, Dasani etc.) each have their own brand names. With companies like Unilever, the corporate brand name takes a back seat to the brands names of the dozens of products controlled by the company, which include Dove (soap), Axe (deodorant), Hellman's (mayonnaise) and Close-up (toothpaste), just to name a few. There are clearly cases of people with significant brand name value, in sports (Ohtani in baseball, Messi in soccer, Kohli in cricket) and entertainment (Taylor Swift, Beyonce), with a spill over to the entities that attach themselves to these people. In fact, a critical component of Nike's brand name was put in place in 1984, when the company signed on Michael Jordan, in his rookie season as a basketball player, and reaped benefits as he became the sport's biggest star over the next decade. Brand names and other Competitive Advantages One reason that brand name discussions often lose their focus is that companies are quick to bundle a host of competitive advantages, each of which may be valuable, in the brand name grouping. The table below, where I have loosely borrowed from Morningstar and Michael Porter is one way to think about both the types and sustainability of competitive advantages: Companies like Walmart and Aramco have significant competitive advantages, but I don't think brand name is on the top five list. Walmart's strengths come from immense economies of scale and bargaining power with suppliers, and Aramco's value derives from massive oil reserves, with far lower costs of extraction, than any of its competitors. Google and Facebook control the advertising business, because they have huge networking benefits, i.e., they become more attractive destinations for advertisers as they get bigger, explaining why they were so quick to change their corporate names, and why it has had so little effect on value. The pharmaceutical companies have some brand name value, but a bigger portion of their value added comes from the protection against competition they get from owning patents. While this may seem like splitting hairs, since all competitive advantages find their way into the bottom line (higher earnings or lower risk), a company that mistakes where its competitive advantages come from risks losing those advantages. Brand Name Value At the risk of drawing backlash from marketing experts and brand name consultants, I will start with my "narrow" definition of brand name. In arriving at this definition, I will fall back on a structure where I connect the value of a business to key drivers, and look at how brand name will affect these drivers: Put simply, brand name value can show up in almost every input, with a more recognizable (and respected) brand name leading to more sales (higher revenues and revenue growth), more pricing power (higher margins), and perhaps even less reinvestment and less risk (lower costs of capital and failure risk). That said, the strongest impact of brand name is on pricing power, with brand name in its purest form allowing it's owner to charge a higher price for a product or service than a competitor could charge for an identical offering. To illustrate, I walked over to my neighborhood pharmacy, and compared the prices of an over-the-counter pain killer (acetaminophen), in its branded form (Tylenol) and its generic version (CVS) : The ingredients, in case you are wondering, are exactly the same, leading to the interesting question, more psychological than financial, of why anyone would pay an extra $2.50 for a product with no differentiating features. If you are wondering how this plays out at the business level, the operating margins of pharmaceutical companies that own the "brand names" are significantly higher than the brand names of companies that make just the generic substitutes. The Tylenol example also serves to illustrate when it is easiest to value brand name, i.e., when it is the only competitive advantage, and when it will become difficult to do, i.e., when it has many competitive advantages. It is for that reason that valuing brand name is easier to do at a beverage or cereal company, such as Coca Cola or Kellogg's, where there is little to differentiate across products other than brand name, and you can attribute the higher margins almost entirely to brand name. It is at the basis for my valuation of Coca Cola's brand name in the picture below, where I value the company with its current operating margin: Coca Cola valuation Note that while the company comes in as slightly overvalued, it is still given a value of $281.15 billion, with much of that value coming from its pre-tax operating margin of 29.73%. We estimate the value of Coca Cola's brand name in two steps, first comparing to a weighted average margin off 16.75% for soft-drink beverage companies, where many of the largest companies are themselves branded (Pepsi, Dr. Pepper etc.), albeit with less pricing power than Coca Coal and then comparing to the median operating margin of 6.92%, skewed towards smaller and generic beverage companies listed globally: Coca Cola valuation This is undoubtedly simplistic, since it assumes that the brand name value shows up entirely in the margin, and it likely understates the value of Coca Cola's brand name. That said, valuing Coca Cola at the median beverage company margin yields a value of $51 billion, suggesting that 82% of the company's intrinsic value comes from its brand name. Comparing to other beverage company and valuing at the weighted average operating margin still yields a differential brand value of $131.4 billion for Coca Cola, indicating that having a premium brand name has significant value. Brand names become more difficult to isolate and value, when a company has multiple competitive advantages, since the higher margins or growth or returns on capital will reflect the composite effect of all of the advantages. With companies like Apple, where brand name is a factor, as is a proprietary operating system, a superior styling and a unique app ecosystem, the higher margin can be attributed to a multitude of factors, making it more difficult, perhaps even impossible, to isolate the brand name value. When valuing Birkenstock, at the time of its IPO, I wrestled with this problem, and with the help of a series of assumptions along the way, did find a way to break the value of the four intangibles that I saw in the company: a world-recognized brand name, a quality management team, free celebrity advertising and the buzz created by Margot Robbie wearing pink Birkenstock in the Barbie movie. Download Birkenstock valuation at the time of IPO The pricing premium effect of brand name also becomes an effective device to strip companies that hold on to the delusion that their brand name values have value, long after they have lost their shine. If a company has margins that trail that of other companies in its industry grouping, it has lost brand name bragging rights (and value), and it is time to either accept that reality or rebrand to acquire pricing power again. Applying this test, you will find that nine out of ten companies that claim to have brand values have really nothing to show for that claim. Nike, in my view, falls somewhere between the two extremes. It is not as pure a brand play as Coca Cola, since athletic footwear, in particular, has physical differentiation that may lead some to prefer one brand over another. At the same time, it is not as complex as Apple, insofar as even a Nike aficionado can find a relatively close substitute in another brand. To measure how Nike's brand name has played out in its operating metrics, we compared the company's operating margins to the weighted operating margin of the two businesses (two thirds footwear and one third apparel) that Nike has operated in for much of the last two decades: Other than 2023, Nike has consistently earned a higher operating margin (1.5% to 3% higher) than the rest of the industry, and since much of this industry is composed of brand name companies, it would suggest that Nike has a premium brand name, not surprisingly. If you are a Nike-pessimist, though, the drop off in the margin differential in the last five years is troubling, but almost all of that drop can be attributed to the company's troubles in 2023. Clearly, the company is taking the decline seriously, bringing back a Nike employee of long standing in Elliott Hill to replace John Donahoe, who cut his teeth in tech companies (ServiceNow, eBay and PayPal). I valued Nike, using its compounded annual growth rate and average operating margin over three period - 2014-2108, 2019-2023 and just the last twelve months: Nike valuation You can see why Nike acted swiftly to change its CEO, since its value will dip substantially, if its growth stays down and margins do not bounce back. At the $71 stock price that the stock was trading at, just six weeks ago, the investing odds would have been in your favor, but the bounce back in the stock price to $88, after the new CEO hire, suggests that the market is pricing in the expectation that the company will bounce back to higher growth and better margins. Brand Name Creation Brand name does add value, if it gives the company that owns it pricing power, but how does a company end up with a valuable brand name? There are facile answers and they include longevity, with long-lived companies having more recognizable brand names, and advertising, where more spending is assumed to result in a more valuable brand name. To see why I attach the "facile" prefix to these answers, consider again the example of AT&T, a company that has been around for more than a century and remains one of the ten largest spenders on advertising in the United States. None of that spending has translated into a significant brand name value, thought there may other benefits that the company accrues. I am sure that someone who immerses themselves in in this topic, perhaps in marketing and advertising, may be able to provide a deeper answer, but here is what I see as ingredients that go into developing a valuable brand name: Attachment to an emotional factor/need: As marketing has recognized through the ages, the key to a powerful brand name is a tie to a human emotion. Rational or not, consumers may reach for a branded product, because they associate the product with freedom, reliability, happiness, patriotism or aspiration, if that association exists in their minds. The challenge, of course, is to find an emotion that attaches well to your product, either because of its history or its make-up, but the association, once made, can be powerful and long-lasting. Celebrity connection: Earlier, we talked about personal brand names, and argued that Nike benefited from its association with Michael Jordan, in building its brand name. In fact, Apple (in its streaming service) and Major League Soccer benefited mightily from Lionel Messi playing Inter Miami, with the former adding hundreds of thousands of subscribers to it soccer streaming service, and the latter increasing attendance in stadiums around the country. Here again, there are perils, since attaching a brand name to a person also exposes the company to the failings and foibles of that person, as Nike found out in its associations with both Tiger Woods and Colin Kaepernick. Fortuitous events/ choices: There is a third factor that is not covered in most brand name management classes, and for good reason, and that is the effect of luck. In an alternate universe, Phil Knight might have stayed with Dimension Six, his initial choice for the company name, picked a different symbol than the swoosh (for which Nike paid $35 to the designer) and even a different slogan ( than the "Just do it" picked by the advertising team), and the end result could have been very different. Advertising: While there may be little or no link between overall advertising spending and brand name, it is undeniable that there are ads that catch people's attention and alter perceptions of a product. I was an Apple user already in 1984, when it ran its famous 1984 ad during the Super Bowl, setting itself apart from the PC makers, and while that ad yielded little monetary benefit to Apple in the immediate aftermath, it contributed to creating the brand name that now allows the company to charge $1600 for a new smart phone. Nike has had its share of iconic commercials, and I still remember this Nike ad, with Michael Jordan, from 1997, showing how long the shelf life can be for a great ad. If asked to advice a company that was intent on creating a brand name, my suggestion would be to start with a product or service that is differentiated from the competition, and to give the brand name time to build around that differentiation. That may require sacrifices on scaling up (accepting less growth to preserve the product differential), a higher cost structure (if it is a quality difference) and perhaps even more reinvestment, but trade offs are inherent to almost everything of value in business. If the expected costs of building a brand name exceed its benefits, though, it may be worth asking whether brand name is the competitive advantage that the company should be aspiring for, since there are other competitive advantages that can add as much or much more value in the business the company operates in. Brand Name Destruction The benefit of building a strong brand name is that it remains one of the most sustainable competitive advantages in business, with the advantages often lasting decades. However, even brand names eventually lose their luster, but the reasons they do so vary: Aging brand/consumer base: In my posts and book on corporate life cycle, I talk about how and why companies age, and how aging is inevitable. The same can be said of brand names, since even the most highly regarded brand names eventually age, and no matter how much managers try to resurrect them, they never recover their mojo. When valuing Kraft Heinz in 2015, when the most venerable name in value investing (Warren Buffett) teamed up with one of the shrewdest players in private equity (3G Capital) to buy the company because it was under valued, I wondered whether the reason the market was turning down on the company was because the portion of the population that were drawn to the company's products (fifty seven types of ketchup, all of which taste bad, and cheese that stays liquid through a nuclear winter) to be tasty was getting smaller and older. In hindsight, it is clear that Kraft Heinz will not reclaim its former glory, because its products and customer base have aged. Benign neglect: Brand names may provide sustainable competitive advantages, but only if they are cared for and maintained. There are legendary brand names that have been neglected, treated as cash cows with no new investment or sprucing up needed, and have faded in value. Quaker Oats, a longstanding mainstay of the US cereal business, not only allowed itself to pushed to the sidelines by aggressive cereal companies, but failed to take advantage of the rise in demand for oatmeal as a heart-healthy substitute. Cultural changes: There are products and services that have lost their allure over time, because the cultural mores or social norms of the consumers have changed. If you binge watch Mad Men, the television series about advertising in the 1960s, you should not be surprised to see ads for products and services that you would now view in a very different light. Changing tastes: There are some businesses, where the demand for products is transient and fad-driven, and new brands replace old ones, as tastes shift. This has generally been the case with apparel retail in the United States, with the Gap's reign at the top lasting about a decade, with newer and cooler retail brands like Abercrombie and Fitch and Tommy Hilfiger replacing them, and then were themselves being displaced by H&M and Uniqlo. Toxic connections: A brand name that is built up over time can sometimes very quickly fall back to earth, if the company or its personnel bring toxic connections. Abercrombie and Fitch, for instance, which became a hot destination for the young in the first decade of this century, found its brand name devastated by accusations of racism and sexism in its ranks. Brand overreach: There are cases where a company with a valuable brand name may dilute or even destroy that brand name by overreaching, and putting it on products that cut agains the brand name narrative. A good argument can be made that Disney, usually masterful at managing its brands, diluted the value of both its Avengers and Star Wars franchises by rushing headlong into the streaming business, with new series. While all of these forces can cause a once valuable brand name to lose its value, it is worth noting that there are companies that have redeemed brand name value, sometimes by remaking the product or service, sometimes by repackaging it and sometimes by repositioning it. Crocs, whose brand name soared in the 2000s, but crashed by the end of the decade, repackaged itself around celebrity endorsements to become a successful brand again. Lego, a venerable brand name in the toy business, sold off its theme parks, and refocused attention on its core product, while redirecting its offerings to adults. In general, though, reincarnating a brand becomes easier for niche brands than for mass market ones, for product brands than for company brands, and for younger brands than for older ones. I believe that 2023 was a wake up call for Nike, as it awoke multiple disruptions. First, in the post-COVID years, Nike moved from store sales to digital sales, with Nike Digital, accounting for almost 43% of revenues in 2022. While that shift does reflect a change in consumer preferences towards shopping online, there is a question of whether bypassing shoe stores, which over the decades have contributed to the Nike brand, by highlighting their most iconic shoes, has undercut the brand. Second, while the footwear business has been more resistant to fads than the apparel business, Nike;'s mass market strategy of being all things to all people is exposing it to disruption. The company is losing market share, especially among younger customers, to newcomers in the space like On and Hoka, and among runners (Nike's original core market) to older companies like New Balance that have rediscovered their mojo. Third, in an age where celebrities come with problems, and politics divides us on even the most trivial of issues, Nike's celebrity-driven advertising campaigns may hurt more than help the company. In short, Nike's new CEO has his work cut out for him! YouTube Video Links Coca Cola - Intrinsic Valuation in September 2024 Nike - Intrinsic Valuation in September 2024 Operating Metrics by Industry group, with distributions (Quartiles and Median)
The big story on Wednesday, September 18, was that the Federal Reserve’s open market committee finally got around to “cutting rates”, and doing so by more than expected. This action, much debated and discussed during all of 2024, was greeted as "big" news, and market prognosticators argued that it was a harbinger of market moves, both in interest rates and stock prices. The market seemed to initially be disappointed in the action, dropping after the Fed’s announcement on Wednesday, but it did climb on Thursday. Overall, though, and this is my view, this was about as anticlimactic as a climactic event gets, akin to watching an elephant in labor deliver a mouse. As a long-time skeptic about the Fed’s (or any Central Bank’s) capacity to alter much in markets or the economy, I decided now would be as good a time as any to confront some widely held beliefs about central banking powers, and counter them with data. In particular, I want to start with the myth that central banks set interest rates, or at least the interest rates that you and I may face in our day-to-day lives, move on to the slightly lesser myth that the Fed's move lead market interest rates, then examine the signals that emanate supposedly from Fed actions, and finish off by evaluating how the Fed's actions affect stock prices. The Fed as Rate Setter As I drove to the grocery story on Fed Cut Wednesday, I had the radio on, and in the news at the top of the hour, I was told that the Fed had just cut interest rates, and that consumers would soon see lower rates on their mortgages and businesses on their loans. That delusion is not restricted to newscasters, since it seems to be widely held among politicians, economists and even market watchers. The truth, though, is that the Fed sets only one interest rate, the Fed Funds rate, and that none of the rates that we face in our lives, either as consumers (on mortgages, credit cards or fixed deposits) or businesses (business loans and bonds), are set by or even indexed to the Fed Funds Rate. The place to start to dispel the “Fed sets rates” myth is with an understanding of the Fed Funds rate, an overnight intra-bank borrowing rate is one that most of us will never ever encounter in our lives. The Federal Open Market Committee (FOMC) has the power to change this rate, which it uses at irregular intervals, in response to economic, market and political developments. The table below lists the rate changes made by the Fed in this century: Note that while most of these changes were made at regularly scheduled meetings, a few (eleven in the last three decades) were made at emergency meetings, called in response to market crises. As you can see from this table, the Federal Reserve goes through periods of Fed Funds rate activism, interspersed with periods of inactivity. Since the Fed Funds rate is specified as a range, there are periods where the effective Fed Funds rate may go up or down, albeit within small bounds. To gain perspective on how the Fed Funds rate has been changed over time, consider the following graph, where the effective fed funds rate is shown from 1954 to 2024: Download data In addition to revealing how much the Fed Funds rate has varied over time, there are two periods that stand out. The first is the spike in the Fed Funds rate to more than 20% between 1979 and 1982, when Paul Volcker was Fed Chair, and represented his attempt to break the cycle of high inflation that had entrapped the US economy. The second was the drop in the Fed Funds rate to close to zero percent, first after the 2008 crisis and then again after the COVID shock in the first quarter of 2020. In fact, coming into 2022, the Fed had kept the Fed Funds rates at or near zero for most of the previous 14 years, making the surge in rates in 2022, in response to inflation, shock therapy for markets unused to a rate-raising Fed. While the Federal Open Market Committee controls the Fed Funds rate, there are a whole host of rates set by buyer and sellers in bond markets. These rates are dynamic and volatile, and you can see them play out in the movements of US treasury rates (with the 3-month and 10-year rates highlighted) and in corporate bond rates (with the Baa corporate bond rate shown). Download data There is a final set of rates, set by institutions, and sometimes indexed to market-set rates, and these are the rates that consumers are most likely to confront in their day-to-day lives. They include mortgage rates, set by lenders, credit card rates, specified by the credit card issuers, and fixed deposit rates on safety deposits at banks. They are not as dynamic as market-set rates, but they change more often than the Fed Funds rate. Download data There are undoubtedly other interest rates you will encounter, as a consumer or a business, either in the course of borrowing money or investing it, but all of these rates will fall into one of three buckets - market-set interest rates, rates indexed to market-set rates and institutionally-set rates. None of these rates are set by the Federal Reserve, thus rendering the "Fed sets interest rates" as myth. Response to comments: It is true that the prime rate remains one of the few that is tied to the Fed Funds rate, and that there is subset of business loans, whose rates are tied to the prime rate. That said, the portion of overall business debt that is tied to the prime rate has declined significantly over time, as variable rate loans have switched to treasury rates as indices, because they tend to be updated and dynamic. It is also true that central-bank set rates can affect a larger subset of rates in some countries, for one of two reasons. The first is that the country has poorly functioning or no bond markets, making market-set rates a non-starter. The second is if the government or central bank can force banks to lend at rates tied to the central bank rate. In both cases, though, the central banking power works only if it is restrained by reality, i.e., the central bank rate reflects the inflation and real growth in the economy. Thus, if inflation is 20%, a central bank that forces lenders to lend at 12% will accomplish one of two objectives - driving lending banks to calamity or drying up the market for business loans. The Fed as Rate Leader Even if you accept that the Fed does not set the interest rates that we face as consumers and businesses, you may still believe that the Fed influences these rates with changes it makes to the Fed Funds rate. Thus, you are arguing that a rise (fall) in the Fed Funds rate can trigger subsequent rises (falls) in both market-set and institution-set rates. At least superficially, this hypothesis is backed up in the chart below, where I brings all the rates together into one figure: Download data As you can see, the rates all seem to move in sync, though market-set rates move more than institution-set rates, which, in turn, are more volatile than the Fed Funds rate. The reason that this is a superficial test is because these rates all move contemporaneously, and there is nothing in this graph that supports the notion that it is the Fed that is leading the change. In fact, it is entirely possible, perhaps even plausible, that the Fed's actions on the Fed Funds rate are in response to changes in market rates, rather than the other way around. To test whether changes in the Fed Funds rate are a precursor for shifts in market interest rates, I ran a simple (perhaps even simplistic) test. I looked at the 249 quarters that compose the 1962- 2024 time period, breaking down each quarter into whether the effective Fed Funds rate increased, decreased or remained unchanged during the quarter. I followed up by looking at the change in the 3-month and 10-year US treasury rates in the following quarter: Download data Looking at the key distributional metrics (the first quartile, the median, the third quartile), it seems undeniable that the "Fed as leader" hypothesis falls apart. In fact, in the quarters after the Fed Funds rate increases, US treasury rates (short and long term) are more likely to decrease than increase, and the median change in rates is negative. In contrast, in the periods after the Fed Fund decreases, treasury rates are more likely to increase than decrease, and post small median increases. Expanding this assessment to the interest rates that consumers face, and in particular mortgage rates at which they borrow and fixed deposit rates at which they can invest, the results are just as stark. Download data In the quarter after the Fed Funds rate increase, mortgage rates and fixed deposit rates are more likely to fall than rise, with the median change in the 15-year mortgage rate being -0.13% and the median change in the fixed deposit rate at -0.05%. In the quarter after the Fed Funds rate decreases, the mortgage rate does drop, but by less than it did during the Fed rate raising quarters. In short, those of us expecting our mortgage rates to decline in the next few months, just because the Fed lowered rates on Wednesday, are being set up for disappointment. If you are wondering why I did not check to see what credit card interest rates do in response to Fed Funds rate changes, even a casual perusal of those rates suggests that they are unmoored from any market numbers. You may still be skeptical about my argument that the Fed is more follower than leader, when it comes to interest rates. After all, you may say, how else can you explain why interest rates remained low for the last decades, other than the Fed? The answer is recognizing that market-set rates ultimately are composed of two elements: an expected inflation rate and an expected real interest rate, reflecting real economic growth. In the graph below, which I have used multiple times in prior posts, I compute an intrinsic risk free rate by just adding inflation rate and real GDP growth each year: Download data Interest rates were low in the last decade primarily because inflation stayed low (the lowest inflation decade in a century) and real growth was anemic. Interest rates rose in 2022, because inflation made a come back, and the Fed scrambled to catch up to markets, and most interesting, interest are down this year, because inflation is down and real growth has dropped. As you can see, in September 2024, the intrinsic riskfree rate is still higher than the 10-year treasury bond rate, suggesting that there will be no precipitous drop in interest rates in the coming months. Response to comments: Some readers are suggesting a plausible, albeit convoluted, rationale for this result that preserves the Fed Delusion. In a version of 4D chess, they argue that investors in bond markets are largely in the business of forecasting what the Fed will do and that market rates move ahead of Fed actions. Besides being extraordinarily unhealthy for bond investing, if this is in fact what it is happening, there are four problems with this reasoning, First, bond markets pre-date central banks setting rates, and they seemed to do a reasonably good job before the Fed Funds rate was around. In fact, I started in investing in the 1980s, when the Fed went into hibernation on the Fed Funds rate, and trust me when I say the bond market did not miss a beat. Second, if the entire point of bond investing is forecasting what the Fed will do, how would you explain the rise in treasury bill and bond rates in the first half of 2024 (just to give one instance), when all the talk was about the Fed cutting rates, not raising them? Third, if bond markets exist to bet on Fed movements, when the Fed moves unexpectedly (by raising or lowering rates more than expected), there should be an immediate adjustment in the bond market? Thus, last week, when the consensus was that a 25 basis point cut was more likely than a 50 basis point one, there should be have a significant drop in treasury rates in the days after, and there was not. The Fed as Signalman If you are willing to accept that the Fed does not set rates, and that it does not lead the market on interest rates, you may still argue that Fed rate changes convey information to markets, leading them to reprice bonds and stocks. That argument is built on the fact that the Fed has access to data about the economy that the rest of us don't have, and that its actions tell you implicitly what it is seeing in that data. It is undeniable that the Federal Reserve, with its twelve regional districts acting as outposts, collects information about the economy that become an input into its decision making. Thus, the argument that Fed actions send signals to the markets has basis, but signaling arguments come with a caveat, which is that the signals can be tough to gauge. In particular, there are two major macroeconomic dimensions on which the Fed collects data, with the first being real economic growth (how robust it is, and whether there are changes happening) and inflation (how high it is and whether it too is changing). The Fed's major signaling device remains the changes in the Fed Funds rate, and it is worth pondering what the signal the Fed is sending when it raises or lowers the Fed Funds rate. On the inflation front, an increase or decrease in the Fed Funds rate can be viewed as a signal that the Fed sees inflationary pressures picking up, with an increase, or declining, with a decrease. On the economic growth front, an increase or decrease in the Fed Funds rate, can be viewed as a signal that the Fed sees the economy growing too fast, with an increase, or slowing down too much, with a decrease. These signals get amplified with the size of the cut, with larger cuts representing bigger signals. Viewed through this mix, you can see that there are two contrary reads of the Fed Funds rate cut of 50 basis points on Wednesdays. If you are an optimist, you could take the action to mean that the Fed is finally convinced that inflation has been vanquished, and that lower inflation is here to stay. If you are a pessimist, the fact that it was a fifty basis point decrease, rather than the expected twenty five basis points, can be construed as a sign that the Fed is seeing more worrying signs of an economic slowdown than have shown up in the public data on employment and growth. There is of course the cynical third perspective, which is that the Fed rate cut has little to do with inflation and real growth, and more to do with an election that is less than fifty days away. In sum, signaling stories are alluring, and you will hear them in the coming days, from all sides of the spectrum (optimists, pessimists and cynics), but the truth lies in the middle, where this rate cut is good news, bad news and no news at the same time, albeit to different groups. Response to comments: Fed rate change signals, as I mentioned, are tough to read. If you have strong priors on the Fed having power to drive markets, you can always the benefit of hindsight to bend the signal to match your priors. The Fed as Equity Market Whisperer It is entirely possible that you are with me so far, in my arguments that the Fed's capacity to influence the interest rates that matter is limited, but you may still hold on to the belief that the Fed's actions have consequences for stock returns. In fact, Wall Street has its share of investing mantras, including "Don't fight the Fed", where the implicit argument is that the direction of the stock market can be altered by Fed actions. There is some basis for this argument, and especially during market crises, where timely actions by the Fed may alter market mood and momentum. During the COVID crisis, I complimented the Fed for playing its cards right, especially so towards the end of March 2020, when markets were melting down, and argued that one reason that market came back as quickly as they did was because of the Fed. That said, it was not so much the 100 basis point drop in the Fed Funds rate that turned the tide, but the accompanying message that the Federal Reserve would become a backstop for lenders to companies that were rocked by the COVID shutdown, and were teetering on the edge. While the Fed did not have to commit much in capital to back up this pledge, that decision seemed to provide enough reassurance to lenders and prevent a host of bankruptcies at the time. If you remove the Fed's role in crisis, and focus on the effects of just its actions on the Fed Funds rate, the effect of the Fed on equity market becomes murkier. I extended the analysis that I did with interest rates to stocks, and looked at the change in the S&P 500 in the quarter after Fed Funds rates were increased, decreased or left unchanged: Download data The S&P 500 did slightly better in quarters after the Fed Funds rate decreased than when the rate increased, but reserved its best performance for quarters after those where there was no change in the Fed Funds rate. At the risk of disagreeing with much of conventional wisdom, is it possible that the less activity there is on the part of the Fed, the better stocks do? I think so, and stock markets will be better served with fewer interviews and speeches from members of the FOMC and less political grandstanding (from senators, congresspeople and presidential candidates) on what the Federal Reserve should or should not do. Response to comments: Here again, the 4D chess argument comes out, where equity markets are so clever and forward-looking, they already incorporate what the Fed will do. Without realizing it, you are making my case that when discussing equity markets and where they will go in the future, we should spend less time talking about what the Fed will do, might do or has not done, since if your premise about markets as forecasting machines is right, it is already in prices. The Fed as Chanticleer If the Fed does not set rates, is not a interest rate driver, sends out murky signals about the economy and has little effect on how stocks move, you are probably wondering why we have central banks in the first place. To answer, I am going to digress, and repeat an ancient story about Chanticleer, a rooster that was anointed the ruler of the farmyard that he lived in, because the other barnyard animals believed that it was his crowing every morning that caused the sun to rise, and that without him, they would be destined for a lifetime of darkness. That belief came from the undeniable fact that every morning, Chanticleer's crowing coincided with sun rise and daylight. The story now takes a dark turn, when one day, Chanticleer sleeps in and the sun rises anyway, revealing his absence of power, and he loses his place at the top of the barnyard hierarchy. The Fed (and every other central bank) in my view is like Chanticleer, with investors endowing it with powers to set interest rates and drive stock prices, since the Fed's actions and market movements seem synchronized. As with Chanticleer, the truth is that the Fed is acting in response to changes in markets rather than driving those actions, and it is thus more follower than leader. That said, there is the very real possibility that the Fed may start to believe its own hype, and that hubristic central bankers may decide that they set rates and drive stock markets, rather than the other way around. That would be disastrous, since the power of the Fed comes from the perception that it has power, and an over reach can lay bare the truth. Response to comments: My comments about the Fed being Chanticleer have been misread by some to imply that central banks do not matter, and Turkey (the country, not the Thanksgiving bird) seems to constantly come up constantly as an example of why central banks matter. Again, you are making my case for me. There is nothing more dangerous to an economy than a central bank that thinks it has the power to override fundamentals and impose its preferred interest rates in the economy. The Turkish central bank, perhaps driven by politics, seems to think that the solution to high interest rates (which are being driven by inflation) is to lower the rates that it controls. Not surprisingly, those actions increase expected inflation, and drive rates higher.... (see definition of insanity..) Conclusion I know that this post cuts against the grain, since the notion that the Fed has superpowers has only become stronger over the last two decades. Pushed to explain why interest rates were at historic lows for much of the last decade, the response you often heard was "the Fed did it". Active investors, when asked why active investing had its worst decade in history, losing out to index funds and to passive investors, pointed fingers at the Fed. Market timers, who had built their reputations around using metrics like the Shiller PE, defended their failure to call market moves in the last fifteen years, by pointing to the Fed. Economists who argued that inverted yield curves were a surefire predictor of recessions blamed the Fed for the absence of a recession, after years of two years plus of the phenomena. I believe that it is time for us to put the Fed delusion to rest. It has distracted us from talking about things that truly matter, which include growing government debt, inflation, growth and how globalization may be feeding into risk, and allowed us to believe that central bankers have the power to rescue us from whatever mistakes we may be making. I am a realist, though, and I am afraid that the Fed Delusion has destroyed enough investing brain cells, that those who holding on to the delusion cannot let go. I am already hearing talk among this group about what the FOMC may or may not do at its next meeting (and the meeting after that), and what this may mean for markets, restarting the Fed Watch. The insanity of it all! YouTube Video Data Fed Funds Rates, Treasury Rates and Other Market Interest rates - Historical Intrinsic treasury bond rates
A few weeks ago, I posted on the corporate life cycle, the subject of my latest book. I argued that the corporate life cycle can explain what happens to companies as they age, and why they have to adapt to aging with their actions and choices. In parallel, I also noted that investors have to change the way they value and price companies, to reflect where they are in the life cycle, and how different investment philosophies lead you to concentrated picks in different phases of the life cycle. In the closing section, I contended that managing and investing in companies becomes most difficult when companies enter the last phases of their life cycles, with revenues stagnating or even declining and margins under pressure. While consultants, bankers and even some investors push companies to reinvent themselves, and find growth again, the truth is that for most companies, the best pathway, when facing aging, is to accept decline, shrink and even shut down. In this post, I will look at three high profile companies, Intel, Starbucks and Walgreens, that have seen market turmoil and management change, and examine what the options are for the future. Setting the stage The three companies that I picked for this post on decline present very different portraits. Intel was a tech superstar not that long ago, a company founded by Gordon Moore, Robert Noyce and Arthur Rock in 1968, whose computer chips have helped create the tech revolution. Walgreens is an American institution, founded in Chicago in 1901, and after its merger with Alliance Boots in 2014, one of the largest pharmacy chains in the country. Finally, Starbucks, which was born in 1971 as a coffee bean wholesaler in Pike Place Market in Seattle, was converted into a coffee shop chain by Howard Schultz, and to the dismay of Italians, has redefined espresso drinks around the world. While they are in very different businesses, what they share in common is that over the recent year or two, they have all not only lost favor in financial markets, but have also seen their business models come under threat, with their operating metrics (revenue growth, margins) reflecting that threat. The Market turns With hundreds of stocks listed and traded in the market, why am I paying attention to these three? First, the companies are familiar names. Our personal computes are often Intel-chip powered, there is a Walgreen's a few blocks from my home, and all of us have a Starbucks around the corner from where we live and work. Second, they have all been in the news in the last few weeks, with Starbucks getting a new CEO, Walgreens announcing that they will be shutting down hundreds of their stores and Intel coming up in the Nvidia conversation, often as a contrast. Third, they have all seen the market turn against them, though Starbucks has had a comeback after its new CEO hire. None of the three stocks has been a winner over the last five years, but the decline in Intel and Walgreen's has been precipitous, especially int he last three years. That decline has drawn the usual suspects. On the one hand are the knee-jerk contrarians, to whom a drop of this magnitude is always an opportunity to buy, and on the other are the apocalyptists, where large price declines almost always end in demise. I am not a fan of either extreme, but it is undeniable that both groups will be right on some stocks, and wrong on others, and the only way to tell the difference is to look at each of the companies in more depth. A Tech Star Stumbles: Intel’s Endgame In my book on corporate life cycles, I noted that even superstar companies age and lose their luster, and Intel could be a case study. The company is fifty six years old (it was founded in 1968) and the question is whether its best years are behind it. In fact, the company's growth in the 1990s to reach the peak of the semiconductor business is the stuff of case studies, and it stayed at the top for longer than most of its tech contemporaries. Intel's CEO for its glory years was Andy Grove, who joined the company on its date of incorporation in 1968, and stayed on to become chairman and CEO before stepping down in 1998. He argued for constant experimentation and adaptive leadership, and the title of his book, "Only the Paranoid Survive", captured his management ethos. To get a measure of why Intel's fortunes have changed in the last decade, it is worth looking at its key operating metrics - revenues, gross income and operating income - over time: As you can see in this graph, Intel's current troubles did not occur overnight, and its change over time is almost textbook corporate life cycle. As Intel has scaled up as a company, its revenue growth has slackened and its growth rate in the last decade (2012-21) is more reflective of a mature company than a growth company. That said, it was a healthy and profitable company during that decade, with solid unit economics (as reflected in its high gross margin) and profitability (its operating margin was higher in the last decade than in prior periods). In the last three years, though, the bottom seems to fallen out of Intel's business model, as revenues have shrunk and margins have collapsed. The market has responded accordingly, and Intel, which stood at the top of the semiconductor business, in terms of market capitalization for almost three decades, has dropped off the list of top ten semiconductor companies in 2024, in market cap terms: Intel's troubles cannot be blamed on industry-wide issues, since Intel's decline has occurred at the same time (2022-2024) as the cumulative market capitalization of semiconductor companies has risen, and one of its peer group (Nvidia) has carried the market to new heights. Before you blame the management of Intel for not trying hard enough to stop its decline, it is worth noting that if anything, they have been trying too hard. In the last few years, Intel has invested massive amounts into its chip manufacturing business (Intel Foundry), trying to compete with TSMC, and almost as much into its new generation of AI chips, hoping to claim market share of the fastest growing markets for AI chips from Nvidia. In fact, a benign assessment of Intel would be that they are making the right moves, but that these moves will take time to pay off, and that the market is being impatient. A not-so-benign reading is that the market does not believe that Intel can compete effectively against either TSMC (on chip manufacture) or Nvidia (on AI chip design), and that the money spent on both endeavors will be wasted. The latter group is clearly winning out in markets, at the moment, but as I will argue in the next section, the question of whether Intel is a good investment at its current depressed price may rest in which group you think has right on its side. Drugstore Blues: Walgreen Wobbles From humble beginnings in Chicago, Walgreen has grown to become a key part of the US health care system as a dispenser of pharmacy drugs and products. The company went public in 1927, and in the century since, the company has acquired the characteristics of a mature company, with growth spurts along the way. Its acquisition of a significant stake in Alliance Boots gave it a larger global presence, albeit at a high price, with the acquisition costing $15.3 billion. Again, to understand, Walgreen's current position, we looked at the company's operating history by looking revenue growth and profit margins over time: After double digit growth from 1994 to 2011, the company has struggled to grow in a business, with daunting unit economics and slim operating margins, and the last three years have only seen things worsen on all fronts, with revenue growth down, and margins slipping further, below the Maginot line; with an 1.88% operating margin, it is impossible to generate enough to cover interest expenses and taxes, thus triggering distress. While management decisions have clearly contributed to the problems, it is also true that the pharmacy business, which forms Walgreen's core, has deteriorated over the last two years, and that can be seen by comparing its market performance to CVS, its highest profile competitor. As you can see, both CVS and Walgreens have seen their market capitalizations drop since mid-2022, but the decline in Walgreens has been far more precipitous than at CVS; Walgreens whose market cap exceeded that of CVS in 2016 currently has one tenth of the market capitalization of CVS. In response to the slowing down of the pharmacy business, Walgreens has tried to find a pathway back to growth, albeit with acquired growth. A new CEO, Roz Brewer, was brought into the company in 2021, from Sam's Club, and wagered the company's future on acquisitions, buying four companies in 2021, with a majority stake in Village MD, a chain of doctor practices and clinics, representing the biggest one. That acquisition, which cost Walgreens $5.2 billion, has been more cash drain than flow, and in 2024, Ms. Brewer was replaced as CEO by Tim Wentworth, and Village MD scaled back its growth plans. Venti no more The Humbling of Starbucks On my last visit to Italy, I did make frequent stops at local cafes, to get my espresso shots, and I can say with confidence that none of them had a caramel macchiato or an iced brown sugar oatmilk shaken espresso on the menu. Much as we make fun of the myriad offerings at Starbucks, it is undeniable that the company has found a way into the daily lives of many people, whose day cannot begin without their favorite Starbucks drink in hand. Early on, Starbucks eased the process by opening more and more stores, often within blocks of each other, and more recently, by offering online ordering and pick up, with rewards supercharging the process. Howard Schultz, who nursed the company from a single store front in Seattle to an ubiquitous presence across America, was CEO of the company from 1986, and while he retired from the position in 2000, he returned from 2008 to 2017, to restore the company after the financial crisis, and again from 2022 to 2023, as an interim CEO to bridge the gap between the retirement of Kevin Johnson in 2022 and the hiring of Laxman Narasimhan in 2023. To get a measure of how Starbucks has evolved over time, I looked the revenues and margins at the company, over time: Unlike Intel and Walgreens, where the aging pattern (of slowing growth and steadying margins) is clearly visible, Starbucks is a tougher case. Revenue growth at Starbucks has slackened over time, but it has remained robust even in the most recent period (2022-2024). Profit margins have actually improved over time, and are much higher than they were in the first two decades of the company's existence. One reason for improving profitability is that the company has become more cautious about store openings, at least in the United States, and sales have increased on a per-store basis: In fact, the shift towards online ordering has accelerated this trend, since there is less need for expansive store locations, if a third or more of sales come from customers ordering online, and picking up their orders. In short, these graphs suggest that it is unfair to lump Starbuck with Intel and Walgreens, since its struggles are more reflecting of a growth company facing middle age. So, why the market angst? The first is that there are some Starbucks investors who continue to hold on to the hope that the company will be able to return to double digit growth, and the only pathway to get there requires that Starbucks be able to succeed in China and India. However, Starbucks has had trouble in China competing with domestic lower-priced competitors (Luckin' Coffee and others), and there are restrictions on what Starbucks can do with its joint venture with the Tata Group in India. The second problem is that the narrative for the company, that Howard Schultz sold the market on, where coffee shops become a gathering spot for friends and acquaintances, has broken down, partly because of the success of its online ordering expansion. The third problem is that inflation in product and employee costs has made its products expensive, leading to less spending even from its most loyal customers. A Life Cycle Perspective It is undeniable that Intel and Walgreens are in trouble, not just with markets but operationally, and Starbucks is struggling with its story line. However, they face different challenges, and perhaps different pathways going forward. To make that assessment, I will more use my corporate life cycle framework, with a special emphasis on the the choices that agin companies face, with determinants on what should drive those choices. The Corporate Life Cycle I won't bore you with the details, but the corporate life cycle resembles the human life cycle, with start-ups (as babies), very young companies (as toddlers), high growth companies (as teenagers) moving on to mature companies (in middle age) and old companies facing decline and demise: The phase of the life cycle that this post is focused on is the last one, and as we will see in the next section, it is the most difficult one to navigate, partly because shrinking as a firm is viewed as failure., and that lesson gets reinforced in business schools and books about business success. I have argued that more money is wasted by companies refusing to act their age, and much of that waste occurs in the decline phase, as companies desperately try to find their way back to their youth, and bankers and consultants egg them on. The Choices There is no more difficult phase of a company's life to navigate than decline, since you are often faced with unappetizing choices. Given how badly we (as human beings) face aging, it should come as no surprise that companies (which are entities still run by human beings) also fight aging, often in destructive ways. In this section, I will start with what I believe are the most destructive choices made by declining firms, move on to a middling choice (where there is a possibility of success) before examining the most constructive responses to aging. a. Destructive Denial: When management of a declining business is in denial about its problems, attributing the decline in revenues and profit margins to extraordinary circumstances, macro developments or bad luck, it will act accordingly, staying with existing practices on investing, financing and dividends. If that management stays in place, the truth will eventually catch up with the company, but not before more money has been sunk into a bad business that is un-investable. Desperation: Management may be aware that their business is in decline, but it may be incentivized, by money or fame, to make big bets (acquisitions, for example), with low odds, hoping for a hit. While the owners of these businesses lose much of the time, the managers who get hits become superstars (and get labeled as turnaround specialists) and increase their earning power, perhaps at other firms. Survival at any cost: In some declining businesses, top managers believe that it is corporate survival that should be given priority over corporate health, and they act accordingly. In the process, they create zombie or walking dead companies that survive, but as bad businesses that shed value over time. b. It depends Me-too-ism: In this choice, management starts with awareness that their existing business model has run out of fuel and faces decline, but believe that a pathway exists back to health (and perhaps even growth) if they can imitate the more successful players in their peer groups. Consequently, their investments will be directed towards the markets or products where success has been found (albeit by others), and financing and cash return policies will follow. Many firms adopt this strategy find themselves at a disadvantage, since they are late to the party, and the winners often have moats that are difficult to broach or a head start that cannot be overcome. For a few firms, imitation does provide a respite and at least a temporary return to mature growth, if not high growth. c. Constructive Acceptance: Some firms accept that their business is in decline and that reversing that decline is either impossible to do or will cost too much capital. They follow up by divesting poor-performing assets, spinning off or splitting off their better-performing businesses, paying down debt and returning more cash to the owners. If they can, they settle in on being smaller firms that can continue to operate in subparts of their old business, where they can still create value, and if this is not possible, they will liquidate and go out of business. Renewals and Revamps: In a renewal (where a company spruces up its existing products to appeal to a larger market) or a revamp (where it adds to its products and service offering to make them more appealing), the hope is that the market is large enough to allow for a return to steady growth and profitability. To pull this off, managers have to be clear eyed about what they offer customers, and recognize that they cannot abandon or neglect their existing customer base in their zeal to find new ones. Rebirths: This is perhaps every declining company's dream, where you can find a new market or product that will reset where the company in the life cycle. This pitch is powered by case studies of companies that have succeeded in pulling off this feat (Apple with the iPhone, Microsoft with Azure), but these successes are rare and difficult to replicate. While one can point to common features including visionary management and organic growth (where the new business is built within the company rather than acquired), there is a strong element of luck even in the success stories. The Determinants Clearly, not all declining companies adopt the same pathway, when faced with decline, and more companies, in my view, take the destructive paths than the constructive one. To understand why and how declining companies choose to do what they do, you may want to consider the following: The Business: A declining company in an otherwise healthy industry or market has better odds for survival and recovery than one that is in a declining industry or bad business. With the three companies in our discussion, Intel's troubles make it an outlier in an otherwise healthy and profitable business (semiconductors), whereas Walgreens operates in a business (brick and mortar retail and pharmacy) that is wounded. Finally, the challenges that Starbucks faces of a saturated market and changing customer demands is common to large restaurants in the United States. Company's strengths: A company that is in decline may have fewer moats than it used to, but it can still hold on to its remaining strengths that draw on them to fight decline. Thus, Intel, in spite of its troubles in recent years, has technological strengths (people, patents) that may be under utilized right now, and if redirected, could add value. Starbucks remains among the most recognized restaurant brands in the world, but Walgreens in spite of its ubiquity in the United States, has almost no differentiating advantages. Governance: The decisions on what a declining firm should do, in the face of decline, are not made by its owners, but by its managers. If managers have enough skin in the game, i.e., equity stakes in the company, their decisions will be often very different than if they do not. In fact, in many companies with dispersed shareholding, management incentives (on compensation and recognition) encourage decision makers to go for long-shot bets, since they benefit significantly (personally) if these bets pay off and the downside is funded by other people's money. Investors: With publicly traded companies, it is the investors who ultimately become the wild card, determining time horizon and feasible options for the company. To the extent that the investors in a declining company want quick payoffs, there will be pressure for companies to accept aging, and shrink or liquidate; that is what private equity investors with enough clout bring to the table. In contrast, if the investors in a declining company have much longer time horizons and see benefits from a turnaround, you are more likely to see revamps and renewals. All three of the companies in our mix are institutionally held, and even at Starbucks, Howard Schultz owns less than 2% of the shares. and his influence comes more from his standing as founder and visionary than from his shareholding. External factors: Companies do not operate in vacuums, and capital markets and governments can become determinants of what they do, when faced with decline. In general, companies that operate in liquid capital markets, where there are multiple paths to raise capital, have more options than companies than operate in markets where capital is scare or difficult to raise. Governments too can play a role, as we saw in the aftermath of the 2008 crisis, when help (and funding) flowed to companies that were too large to fail, and that we see continually in businesses like the airlines, where even the most damaged airline companies are allowed to limp along. Luck: Much as we would like to believe that our fates are in our own hands, the truth is that even the best-thought through response to decline needs a hefty dose of luck to succeed. In the figure below, I summarize the discussion from this section, looking at both the choices that companies can make, and the determinants: With this framework in place, I am going to try to make my best judgments (which you may disagree with) on what the three companies highlighted in this post should do, and how they will play out for me, as an investor: Intel: It is my view that Intel's problems stem largely from too much me-too-ism and aspiring for growth levels that they cannot reach. On both Ai and the chip manufacturing business, Intel is going up against competition (Nvidia on AI and TSMC on manufacturing) that has a clear lead and significant competitive advantages. However, the market is large enough and has sufficient growth for Intel to find a place in both, but not as a leader. For a company that is used to being at the top of the leaderboard, that will be a step down, but less ambition and more focus is what fits the company, at this stage in the life cycle. It is likely that even if it succeeds, Intel will revert to middle age, not high growth, but that should still make it a good investment. In the table below, you can see that at its prevailing stock price of $18.89 (on Sept 8, 2024), all you need is a reversion back towards more normal margins for the price to be justified: Download Intel valuation With 3% growth and 25% operating margins, Intel's value per share is already at $23.70 and any success that the company is in the AI chip market or benefits it derives from the CHIPs act, from federal largesse, are icing on the cake. I do believe that Intel will derive some payoff from both, and I am buying Intel, to twin with what is left of my Nvidia investment from six years ago. Walgreens: For Walgreens, the options are dwindling, as its core businesses face challenges. That said, and even with its store closures, Walgreens remains the second largest drugstore chain in the United States, after CVS. Shrinking its presence to its most productive stores and shedding the rest may be the pathway to survival, but the company will have to figure out a way to bring down its debt proportionately. There is the risk that a macro slowdown or a capital market shock, causing default risk and spreads to widen, could wipe out equity investors. With all of that said, and building in a risk of failure to the assessment, I estimated the value per share under different growth and profitability assumptions: Download Walgreens valuation The valuation pivots entirely on whether operating margins improve to historical levels, with margins of 4% or higher translating into values per share that exceed the stock price. I believe that the pharmacy business is ripe for disruption, and that the margins will not revert back to pre-2021 levels, making Walgreens a "no go" for me. Starbucks: Starbucks is the outlier among the three companies, insofar as its revenue growth is still robust and it remains a money-making firm. Its biggest problem is that it has lost its story line, and it needs to rediscover a narrative that can not only give investors a sense of where it is going, but will redirect how it is managed. As I noted in my post on corporate life cycle, story telling requires visionaries, and in the case of Starbucks, that visionary also has to understand the logistical challenges of running coffee shops. I do not know enough about Brian Niccol to determine whether he fits the bill. As someone who led Taco Bell and Chipotle, I think that he can get the second part (understanding restaurant logistics) nailed down, but is he a visionary? He might be, but visionary CEOs generally do not live a thousand miles from corporate headquarters, and fly corporate jets to work part time at their jobs, and Niccol has provided no sense of what he sees as the new Starbucks narrative yet. For the moment, thought, there seems to be euphoria in the market that change is coming, though no one seems clear on what that change is, and the stock price has almost fully recovered from its swoon to reach $91 on September 8, 2024. That price is well above any value per share that I can get for the company, even assuming that they go back to historic norms: I must be missing some of the Starbucks magic that investors are seeing, since there is no combination of historical growth/margins that gets me close to the current stock price. In fact, the only way my value per share reaches current pricing levels is if I see the company maintaining its revenue growth rates from 2002-2011, while delivering the much higher operating margins that it earned between 2012-2021. That, to me, is a bridge too far to cross. The Endgame There is a reason that so many people want to be entrepreneurs and start new businesses. Notwithstanding the high mortality rate, building a new business is exciting and, if successful, hugely rewarding. A healthy economy will encourage entrepreneurship, providing risk capital and not tilting the playing field towards established players; it remains the strongest advantage that the United States has over much of the rest of the world. However, it is also true that the measure of a healthy economy is in how it deals with declining businesses and firms. If as Joseph Schumpeter put it, capitalism is all about creative destruction, it follows that companies, which are after all legal entities that operate businesses, should fade away as the reasons for their existence fade. That is one reason I critique the entire notion of corporate sustainability (as opposed to planet sustainability), since keeping declining companies alive, and supplying them with additional capital, redirects that capital away from firms that could do far more good (for the economy and society) with that capital. If there is a subtext to this post, it is that we need a healthier framing of corporate decline, as inevitable at all firms, at some stage in their life cycle, rather than something that should be fought. In business schools and books, we need to highlight not just the empire builders and the company saviors, i.e., CEOs who rescued failing companies and made their companies bigger, but the empire shrinkers, i.e., CEOs who are brought into declining firms, who preside over an orderly (and value adding) shrinkage or breaking of their firms. In investing, it is true that the glory gets reserved for the Mag Seven and the FANGAM stocks, companies that seem to have found the magic to keep growing even as they scale up, but we should also pay attention to companies that find their way to deliver value for shareholders in bad businesses. YouTube Video Links Corporate Life Cycle (my blog post) Corporate Life Cycle (my book) Valuations Intel in September 2024 Walgreens in September 2024 Starbucks in September 2024
Last Wednesday (August 28), the market waited with bated breath for Nvidia’s earning call, scheduled for after the market closed. That call, at first sight, contained exceptionally good news, with revenues and earnings coming in at stratospheric levels, and above expectations, but the stock fell in the aftermath, down 8% in Thursday’s trading. That drop of more than $200 billion in market capitalization in response to what looked like good news, at least on the surface, puzzled market observers, though, as is their wont, they had found a reason by day end. This dance between companies and investors, playing out in expected and actual earnings, is a feature of every earnings season, especially so in the United States, and it has always fascinated me. In this post, I will use the Nvidia earnings release to examine what news, if any, is contained in earnings reports, and how traders and investors use that news to reframe their thinking about stocks. Earnings Reports: The Components When I was first exposed to financial markets in a classroom, I was taught about information being delivered to markets, where that information is processed and converted into prices. I was fascinated by the process, an interplay of accounting, finance and psychology, and it was the subject of my doctoral thesis, on how distortions in information delivery (delays, lies, mistakes) affects stock returns. In the real world, that fascination has led me to pay attention to earnings reports, which while overplayed, remain the primary mechanism for companies to convey information about their performance and prospects to markets. The Timing Publicly traded companies have had disclosure requirements for much of their existence, but those requirements have become formalized and more extensive over time, partly in response to investor demands for more information and partly to even the playing field between institutional and individual investors. In the aftermath of the great depression, the Securities Exchange Commission was created as part of the Securities Exchange Act, in 1934, and that act also required any company issuing securities under that act, i.e., all publicly traded firms, make annual filings (10Ks) and quarterly filings (10Qs), that would be accessible to investors. The act also specifies that these filings be made in a timely manner, with a 1946 stipulation the annual filings being made within 90 days of the fiscal year-end, and the quarterly reports within 45 calendar days of the quarter-end. With technology speeding up the filing process, a 2002 rule changed those requirements to 60 days, for annual reports, and 40 days for quarterly reports, for companies with market capitalizations exceeding $700 million. While there are some companies that test out these limits, most companies file well within these deadlines, often within a couple of weeks of the year or quarter ending, and many of them file their reports on about the same date every year. If you couple the timing regularity in company filings with the fact that almost 65% of listed companies have fiscal years that coincide with calendar years, it should come as no surprise that earnings reports tend to get bunched up at certain times of the year (mid-January, mid-April, mid-July and mid-October), creating “earnings seasons”. That said, there are quite a few companies, many of them high-profile, that preserve quirky fiscal years, and since Nvidia’s earnings report triggered this post, it is worth noting that Nvidia has a fiscal year that ends on January 31 of each year, with quarters ending on April 30, July 31 and October 31. In fact, the Nvidia earnings report on August 28 covered the second quarter of this fiscal year (which is Nvidia's 2025 fiscal year). The Expectations Game While corporate earnings reports are delivered once a quarter, the work of anticipating what you expect these reports to contain, especially in terms of earnings per share, starts almost immediately after the previous earnings report is delivered. In fact, a significant portion of sell side equity research is dedicated to this activity, with revisions made to the expected earnings, as you get closer and closer to the next earnings report. In making their earnings judgments and revisions, analysts draw on many sources, including: The company’s history/news: With the standard caveat that the past does not guarantee future results, analysts consider a company’s historical trend lines in forecasting revenues and earnings. This can be augmented with other information that is released by the company during the course of the quarter. Peer group reporting: To the extent that the company’s peer group is affected by common factors, it is natural to consider the positive or negative the operating results from other companies in the group, that may have reported earnings ahead of your company. Other analysts’ estimates: Much as analysts claim to be independent thinkers, it is human nature to be affected by what others in the group are doing. Thus, an upward revision in earnings by one analyst, especially an influential one, can lead to revisions upwards on the part of other analysts. Macro news: While macroeconomic news (about the economy, inflation or currency exchange rates) cuts across the market, in terms of impact, some companies are more exposed to macroeconomic factors than others, and analysts will have to revisit earnings estimates in light of new information. The earnings expectations for individual companies, from sell side equity research analysts are publicly accessible, giving us a window on trend lines. Nvidia is one of the most widely followed companies in the world, and most of the seventy plus analysts who publicly follow the firm play the estimation game, leading into the earnings reports. Ahead of the most recent second quarter earnings report, the analyst consensus was that the company would report revenues of $28.42 billion for the quarter, and fully diluted earnings per share of 64 cents; in the 30 days leading into the report, the earnings estimates had drifted up mildly (about 0.1%), with the delay in the Blackwell (NVidia’s new AI chip) talked about but not expected to affect revenue growth near term. It is worth noting that not all analysts tracking the stock forecast every metric, and that there was disagreement among them, which is also captured in the range on the estimates; on earnings per share, for instance, the estimates ranged from 60 to 68 cents, and on revenues, from $26 to $30 billion. The pre-game show is not restricted to analysts and investors, and markets partake in the expectations game in two ways. Stock prices adjust up or down, as earnings expectations are revised upwards or downwards, in the weeks leading up to the earnings report. Nvidia, which traded at $104 on May 23rd, right after the company reported its results for the first quarter of 2024, had its ups and down during the quarter, hitting an all-time high of $135.58 on June 18, 2024, and a low of $92.06, on August 5, before ending at $125.61 on August 28, just ahead of the earnings report: During that period, the company also split its shares, ten to one, on June 10, a week ahead of reaching its highs. Stock volatility can also changes, depending upon disagreements among analysts about expected earnings, and the expected market reaction to earnings surprises. That effect is visible not only in observed stock price volatility, but also in the options market, as implied volatility. For Nvidia, there was clearly much more disagreement among investors about the contents of the second quarter earnings report, with implied volatility spiking in the weeks ahead of the report: Source: Fintel While volatility tends to increase just ahead of earnings reports, the surge in volatility ahead of the second quarter earnings for Nvidia was unusually large, a reflection of the disagreement among investors about how the earnings report would play out in the market. Put simply, even before Nvidia reported earnings on August 28, markets were indicating more unease about both the contents of the report and the market reaction to the report, than they were with prior earnings releases. The Event Given the lead-in to earnings reports, what exactly do they contain as news? The SEC strictures that companies disclose both annual and quarterly results have been buffered by accounting requirements on what those disclosures should contain. In the United States, at least, quarterly reports contain almost all of the relevant information that is included in annual reports, and both have suffered from the disclosure bloat that I called attention to in my post on disclosure diarrhea. Nvidia’s second quarter earnings report, weighing in at 80 pages, was shorter than its annual report, which ran 96 pages, and both are less bloated than the filings of other large market-cap companies. The centerpieces of the earnings report, not surprisingly, are the financial statements, as operating numbers are compared to expectations, and Nvidia’s second quarter numbers, at least at first sight, are dazzling: The company’s astonishing run of the last few years continues, as its revenues, powered by AI chip sales, more than doubled over the same quarter last year, and profit margins came in at stratospheric levels. The problem, though, is that the company's performance over the last three quarters, in particular, have created expectations that no company can meet. While it is just one quarter, there are clear signs of more slowing to come, as scaling will continue to push revenue growth down, the unit economics will be pressured as chip manufacturers (TSMC) push for a larger slice and operating margins will decrease, as competition increases. Over the last two decades, companies have supplemented the financial reports with guidance on key metrics, particularly revenues, margins and earnings, in future quarters. That guidance has two objectives, with the first directed at investors, with the intent of providing information, and the second at analysts, to frame expectations for the next quarter. As a company that has played the expectations game well, it should come as no surprise that Nvidia provided guidance for future quarters in its second quarter report, and here too, there were reminders that comparisons would get more challenging in future quarters, as they predicted that revenue growth rates would come back to earth, and that margins would, at best, level off or perhaps even decline. Finally, in an overlooked news story, Nvidia announced that it would had authorized $50 billion in buybacks, over an unspecified time frame. While that cash return is not surprising for a company that has became a profit machine, it is at odds with the story that some investors were pricing into the stock of a company with almost unlimited growth opportunities in an immense new market (AI). Just as Meta and Alphabet’s dividend initiations signaled that they were approaching middle age, Nvidia’s buyback announcement may be signaling that the company is entering a new phase in the life cycle, intentionally or by accident. The Scoring The final piece of the earning release story, and the one that gets the most news attention, is the market reaction to the earnings reports. There is evidence in market history that earnings reports affect stock prices, with the direction of the effect depending on how actual earnings measure up to expectations. While there have been dozens of academic papers that focus on market reactions to earnings reports, their findings can be captured in a composite graph that classifies earnings reports into deciles, based upon the earnings surprise, defined as the difference between actual and predicted earnings: As you can see, positive surprises cause stock prices to increase, whereas negative surprises lead to price drops, on the announcement date, but there is drift both before and after surprises in the same direction. The former (prices drifting up before positive and down before negative surprises) is consistent with the notion that information about earnings surprises leaks to markets in the days before the report, but the latter (prices continuing to drift up after positive or down after negative surprises) indicates a slow-learning market that can perhaps be exploited to earn excess returns. Breaking down the findings on earnings reports, there seems to be evidence that the that the earnings surprise effect has moderated over time, perhaps because there are more pathways for information to get to markets. Nvidia is not only one of the most widely followed and talked about stocks in the market, but one that has learned to play the expectations game well, insofar as it seems to find a way to beat them consistently, as can be seen in the following table, which looks at their earnings surprises over the last 5 years: Nvidia Earnings Surprise (%) Barring two quarters in 2022, Nvidia has managed to beat expectations on earnings per share every quarter for the last five years. There are two interpretations of these results, and there is truth in both of them. The first is that Nvidia, as with many other technology companies, has enough discretion in both its expenditures (especially in R&D) and in its revenue recognition, that it can use it to beat what analysts expect. The second is that the speed with which the demand for AI chips has grown has surprised everyone in the space (company, analysts, investors) and that the results reflect the undershooting on forecasts. Focusing specifically on the 2025 second quarter, Nvidia beat analyst expectations, delivering earnings per share of 68 cents (above the 64 cents forecast) and revenues of $30 billion (again higher than the $28.4 billion forecast), but the percentage by which it beat expectations was smaller than in the most recent quarters. That may sound like nitpicking, but the expectations game is an insidious one, where investors move the goal posts constantly, and more so, if you have been successful in the past. On August 28, after the earnings report, Nvidia saw share prices drop by 8% and not only did that loss persist through the next trading day, the stock has continued to lose ground, and was trading at $106 at the start of trading on September 6, 2028. Earnings Reports: Reading the Tea Leaves So what do you learn from earnings reports that may cause you to reassess what a stock is worth? The answer will depend upon whether you consider yourself more of a trader or primarily an investor. If that distinction is lost on you, I will start this section by drawing the contrast between the two approaches, and what each approach is looking for in an earnings report. Value versus Price At the risk of revisiting a theme that I have used many times before, there are key differences in philosophy and approach between valuing an asset and pricing it. The value of an asset is determined by its fundamentals – cash flows, growth and risk, and we attempt to estimate that value by bringing in these fundamentals into a construct like discounted cash flow valuation or a DCF. Looking past the modeling and the numbers, though, the value of a business ultimately comes from the story you tell about that business, and how that story plays out in the valuation inputs. The price of an asset is set by demand and supply, and while fundamentals play a role, five decades of behavioral finance has also taught us that momentum and mood have a much greater effect in pricing, and that the most effective approach to pricing an asset is to find out what others are paying for similar assets. Thus, determining how much to pay for a stock by using a PE ratio derived from looking its peer group is pricing the stock, not valuing it. The difference between investing and trading stems from this distinction between value and price. Investing is about valuing an asset, buying it at a price less than value and hoping that the gap will close, whereas trading is almost entirely a pricing game, buying at a low price and selling at a higher one, taking advantage of momentum or mood shifts. Given the very different perspectives the two groups bring to markets, it should come as no surprise that what traders look for in an earnings report is very different from what investors see in that same earnings report. Earnings Reports: The Trading Read If prices are driven by mood and momentum, it should come as no surprise that what traders are looking for in an earnings report are clues about how whether the prevailing mood and momentum will prevail or shift. It follows that traders tend to focus on the earnings per share surprises, since its centrality to the report makes it more likely to be a momentum-driver. In addition, traders are also swayed more by the theater around how earnings news gets delivered, as evidenced, for instance, by the negative reaction to a recent earnings report from Tesla, where Elon Musk sounded downbeat, during the earnings call. Finally, there is a significant feedback loop, in pricing, where the initial reaction to an earnings report, either online or in the after market, can affect subsequent reaction. As a trader, you may learn more about how an earnings report will play out by watching social media and market reaction to it than by poring over the financial statements. For Nvidia, the second quarter report contained good news, if good is defined as beating expectations, but the earnings beat was lower than in prior quarters. Coupled with sober guidance and a concern the stock had gone up too much and too fast, as its market cap had increased from less than half a trillion to three trillion over the course of two years, the stage was set for a mood and momentum shift, and the trading since the earnings release indicates that it has happened. Note, though, that this does not mean that something else could not cause the momentum to shift back, but before you, as an Nvidia manager or shareholder, are tempted to complain about the vagaries of momentum, recognize that for much of the last two years, no stock has benefited more from momentum than Nvidia. The Investing Read For investors, the takeaways from earnings reports should be very different. If value comes from key value inputs (revenues growth, profitability, reinvestment and risk), and these value inputs themselves come from your company narrative, as an investor, you are looking at the earnings reports to see if there is information in them that would change your core narrative for the company. Thus, an earnings report can have a significant effect on value, if it significantly changes the growth, profitability or risk parts of your company’s story, even though the company’s bottom line (earnings per share) might have come in at expectations. Here are a few examples: A company reporting revenue growth, small or even negligible for the moment, but coming from a geography or product that has large market potential, can see its value jump as a consequence. In 2012, I reassessed the value of Facebook upwards, a few months after it had gone public and seen its stock price collapse, because its first earnings report, while disappointing in terms of the bottom line, contained indications that the company was starting to succeed in getting its platform working on smart phones, a historical weak spot for the firm. You can also have a company reporting higher than expected revenue growth accompanied by lower than anticipated profit margins, suggesting a changing business model, and thus a changed story and valuation. Earlier this year, I valued Tesla, and argued that their lower margins, while bad news standing alone, was good news if your story for Tesla was that it would emerge as a mass market automobile company, capable of selling more cars than Volkswagen and Toyota. Since the only pathway to that story is with lower-priced cars, the Tesla strategy of cutting prices was in line with that story, albeit at the expense of profit margins. A company reporting regulatory or legal actions directed against it, that make its business model more costly or more risky to operate, even though its current numbers (revenues, earnings etc.) are unscathed (so far). In short, if you are an investor, the most interesting components of the report are not in the proverbial bottom line, i.e., whether earnings per share came in below or above expectations, but in the details. Finally, as investors, you may be interested in how earnings reports change market mood, usually a trading focus, because that mood change can operate as a catalyst that causes the price-value gap to close, enriching you in the process. The figure below summarizes this section, by first contrasting the value and pricing processes, and then looking at how earnings releases can have different meanings to different market participants. As in other aspects of the market, it should therefore come as no surprise that the same earnings report can have different consequences for different market participants, and it is also possible that what is good news for one group (traders) may be bad news for another group (investors). Nvidia: Earnings and Value My trading skills are limited, and that I am incapable of playing the momentum game with any success. Consequently, I am not qualified to weigh in on the debate on whether the momentum shift on Nvidia is temporary or long term, but I will use the Nvidia second quarter earnings report as an opportunity to revisit my Nvidia story and to deliver a September 2024 valuation for the company. My intrinsic valuation models are parsimonious, built around revenue growth, profit margins and reinvestment, and I used the second quarter earnings report to review my story (and inputs) on each one: Nvidia: Valuation Inputs (Sept 2024) With these input changes in place, I revalued Nvidia at the start of September 2024, breaking its revenues, earnings and cash flows down into three businesses: an AI chip business that remains its central growth opportunity, and one in which it has a significant lead on the competition, an auto chip business where it is a small player in a small game, but one where there is potential coming from demand for more powerful chips in cars, and the rest, including its existing business in crypto and gaming, where growth and margins are solid, but unlikely to move dramatically. While traders may be disappointed with Nvidia’s earnings release, and wish it could keep its current pace going, I think it is both unrealistic and dangerous to expect it to do so. In fact, one reason that my story for Nvidia has become more expansive, relative to my assessment in June 2023, is that the speed with which AI architecture is being put in place is allowing the total market to grow at a rate far faster than I had forecast last year. In short, relative to where I was about a year ago, the last four earnings reports from the company indicate that the company can scale up more than I thought it could, has higher and more sustainable margins than I predicted and is perhaps less exposed to the cycles that the chip business has historically been victimized by. With those changes in place, my value per share for Nvidia in is about $87, still about 22% below the stock price of $106 that the stock was trading at on September 5, 2024, a significant difference but one that is far smaller than the divergence that I noted last year. Download spreadsheet As always, the normal caveats apply. The first is that I value companies for myself, and while my valuations drive my decisions to buy or sell stocks, they should not determine your choices. That is why my Nvidia valuation spreadsheet is available not just for download, but for modification, to allow you to tell your own story for Nvidia, yielding a different value and decision. The second is that this is a tool for investors, not traders, and if you are playing the trading game, you will have to reframe the analysis and think in terms of mood and momentum. Looking back, I am at peace with the decision made in the summer of 2023 to shed half my Nvidia shares, and hold on to half. While I left money on the table, with the half that I sold, I have been richly compensated for holding on to the other half. I am going to count that as a win and move on! YouTube Video Links Nvidia 10 Q (for 2nd Quarter 2025) Nvidia Valuation (updated on September 5, 2024)
It seems like a lifetime has passed since artificial intelligence (AI) became the market's biggest mover, but Open AI introduced the world to ChatGPT on November 30, 2022. While ChatGPT itself represented a low-tech variation of AI, it opened the door to AI not only as a business driver, but one that had the potential to change the way we work and live. In a post on June 30, 2023, I looked at the AI effect on businesses, arguing that it had the potential to ferment revolutionary change, but that it would also create a few big winners, a whole host of wannabes, and many losers, as its disruption worked its way through the economy. In this post, I would like to explore that disruption effect, but this time at a personal level, as we are warned that we risk being displaced by our AI counterparts. I want to focus on that question, trying to find the middle ground between irrational terror, where AI consigns us all to redundancy, and foolish denial, where we dismiss it as a fad. The Damodaran Bot I was in the eleventh week of teaching my 2024 spring semester classes at Stern, when Vasant Dhar, who teaches a range of classes from machine learning to data science at NYU's Stern School (where I teach as well), and has forgotten more about AI than I will ever know, called me. He mentioned that he had developed a Damodaran Bot, and explained that it was an AI creation, which had read every blog post that I had ever written, watched every webcast that I had ever posted and reviewed every valuation that I had made public. Since almost everything that I have ever written or done is in the public domain, in my blog, YouTube videos and webpage, that effectively meant that my bot was better informed than I was about my own work, since its memory is perfect and mine is definitely not. He also went on to tell me that the Bot was ready for a trial run, ready to to value companies, and see how those valuations measured up against valuations done by the best students in my class. The results of the contest are still being tabulated, and I am not sure what results I would like to see, since either of the end outcomes would reflect poorly on me. If the Bot's valuations work really well, i.e., it values companies as well, or better, than the students in my class, that is about as strong a signal that I am facing obsolescence, that I can get. If the Bot's valuations work really badly, that would be a reflection that I have failed as a teacher, since the entire rationale for my postings and public valuations is to teach people how to do valuation. Gauging the threat In the months since I was made aware of the Damodaran Bot, I have thought in general terms about what AI will be able to do as well or better than we can, and the areas where it might have trouble. Ultimately, AI is the coming together of two forces that have become more powerful over the last few decades. The first is increasing (and cheaper) computing power, often coming into smaller and smaller packages; our phones are now computationally more powerful than the very first personal computers. The second is the cumulation of data, both quantitative and qualitative, especially with social media accelerating personal data sharing. As an AI novice, it is entirely possible that I am not gauging the threat correctly, but there are three dimensions on which I see the AI playing out (well or badly). Mechanical/Formulaic vs Intuitive/Adaptable: Well before ChatGPT broke into the public consciousness, IBM's Deep Blue was making a splash playing chess, and beating some of the world's greatest chess players. Deep Blue's strength at chess came from the fact that it had access to every chess game ever played (data) and the computing power to evaluate 200 million chess positions per second, putting even the most brilliant human chess player at a disadvantage. In contrast, AI has struggled more with automated driving, not because driving is mechanically complicated, but because there are human drivers on the surface roads, behaving in unpredictable ways. While AI is making progress on making intuitive leaps, and being adaptable, it will always struggle more on those tasks than on the purely mechanical ones. Rules-based vs Principle-based: Expanding the mechanical/intuitive divide, AI will be better positioned to work smoothly in rules-based disciplines, and will be at a disadvantage in principle-based disciplines. Using valuation to illustrate my point, accounting and legal valuations are mostly rule-based, with the rules sometimes coming from theory and practice, and sometimes from rule writers drawing arbitrary lines in the sand. AI can not only replicate those valuations, but can do so at no cost and with a much closer adherence to the rules. In contrast, financial valuations done right, are built around principles, requiring judgment calls and analytical choices on the part of appraisers, on how these principles get applied, and should be more difficult to replace with AI. Biased vs Open minded: There is a third dimension on which we can look at how easy or difficult it will be for AI to replace humans and that is in the human capacity to bring bias into decisions and analyses, while claiming to be objective and unbiased. Using appraisal valuation to illustrate, it is worth remembering that clients often come to appraisers, especially in legal or accounting settings, with specific views about what they would like to see in their valuations, and want affirmation of those views from their appraisers, rather than the objective truth. A business person valuing his or her business, ahead of a divorce, where half the estimated value of that business has to be paid out to a soon-to-be ex-spouse, wants a low value estimate, not a high one, and much as the appraiser of the business will claim objectivity, that bias will find its way into the numbers and value. It is true that you can build AI systems to replicate this bias, but it will be much more difficult to convince those systems that the appraisals that emerge are unbiased. Bringing this down to the personal, the threat to your job or profession, from AI, will be greater if your job is mostly mechanical, rule-based and objective, and less if it is intuitive, principle-based and open to biases. Responding to AI While AI, at least in its current form, may be unable to replace you at your job, the truth is that AI will get better and more powerful over time, and it will learn more from watching what you do. So, what can we do to make it more difficult to be outsourced by machines or replaced by AI? It is a question that I have thought about for three decades, as machines have become more powerful, and data more ubiquitous, and while I don't have all of the answers, I have four thoughts. Generalist vs Specialist: In the last century, we have seen a push towards specialization in almost every discipline. In medicine, the general practitioner has become the oddity, as specialists abound to treat individual organs and diseases, and in finance, there are specialists in sub-areas that are so esoteric that no one outside those areas can even comprehend the intricacies of what they do. In the process, there are fewer and fewer people who are comfortable operating outside their domains, and humanity has lost something of value. It is the point I made in 2016, after a visit to Florence, where like hundreds of thousands of tourists before me, I marveled at the beauty of the Duomo, one of the largest free-standing domes in the world, at the time of its construction. The Duomo built by Filippo Brunelleschi, an artist who taught himself enough engineering and construction to be able to build the dome, and he was carrying on a tradition of others during that period whose interests and knowledge spanned multiple disciplines. In a post right after the visit, I argued that the world needed more Renaissance men (and women), individuals who can operate across multiple disciplines, and with AI looming as a threat, I feel even more strongly about this need. A Leonardo Da Vinci Bot may be able to match the master in one of his many dimensions (painter, sculptor, scientist), but can it span all of them? I don't think so! Practice bounded story telling: Starting about a decade ago, I drew attention to a contradiction at the heart of valuation practice, where as access to data and more powerful models has increased, in the last few decades, the quality of valuations has actually become worse. I argued that one reason for that depletion in quality is that valuations have become much too mechanical, exercises in financial modeling, rather than assessments of business quality and value. I went on to make the case that good valuations are bridges between stories and numbers, and wrote a book on the topic. At the time of the book's publication, I wrote a post on why I think stories make valuations richer and better, and with the AI threat looming, connecting stories to numbers comes with a bonus. If your valuation is all about extrapolating historical data on a spreadsheet, AI can do it quicker, and with far fewer errors than you can. If, however, your valuation is built around a business story, where you have considered the soft data (management quality, the barriers to entry), AI will have a tougher time replicating what you do. Reasoning muscle: I have never been good at reading physical maps, and I must confess that I have completely lost even my rudimentary map reading skills, having become dependent on GPS to get to where I need to go. While this inability to read maps may not make or break me, there are other skills that we have has human beings, where letting machines step in and help us, because of convenience and speed, will have much worse long term consequences. In an interview I did on teaching a few years, I called attention to the "Google Search" curse, where when faced with a question, we often are quick to look up the answer online, rather than try to work out the answer. While that is benign, if you are looking up answers to trivia, it can be malignant, when used to answer questions that we should be reasoning out answers to, on our own. That reasoning may take longer, and sometimes even lead you to the wrong answers, but it is a learned skill, and one that I am afraid that we risk losing, if we let it languish. You may think that I am overreacting, but evolution has removed skill sets that we used to use as human beings, when we stopped using or needing them, and reasoning may be next on the list. Wandering mind: An empty mind may the devil's workshop, at least according to puritans, but it is also the birthplace for creativity. I have always marveled at the capacity that we have as human beings to connect unrelated thoughts and occurrences, to come up with marvelous insights. Like Archimedes in his bath and Newton under the apple tree, we too can make discoveries, albeit much weighty ones, from our own ruminations. Again, making this personal, two of my favorite posts had their roots in unrelated activities. The first one, Snowmen and Shovels, emerged while I was shoveling snow after a blizzard about a decade ago, and as I and my adult neighbors struggled dourly with the heavy snow, our kids were out building snowmen, and laughing. I thought of a market analogy, where the same shock (snowstorm) evokes both misery (from some investors) and joy (on the part of others), and used it to contest value with growth investing. The second post, written more recently, came together while I walked my dog, and pondered how earthquakes in Iceland, a data leak at a genetics company and climate change affected value, and that became a more general discourse on how human beings respond (not well) to the possibility of catastrophes. It is disconcerting that on every one of these four fronts, progress has made it more difficult rather than less so, to practice. In fact, if you were a conspiracy theorist, you could spin a story of technology companies conspiring to deliver us products, often free and convenient to use, that make us more specialized, more one dimensional and less reason-based, that consume our free time. This may be delusional on my part, but if want to keep the Damodaran Bot at bay, and I take these lessons to heart, I should continue to be a dabbler in all that interests me, work on my weak side (which is story telling), try reasoning my way to answers before looking them up online and take my dog for more walks (without my phone accompanying me). Beat your bot! I am in an unusual position, insofar as my life’s work is in the public domain, and I have a bot with my name on it not only tracking all of that work, but also shadowing me on any new work that I do. In short, my AI threat is here, and I don’t have the choice of denying its existence or downplaying what it can do. Your work may not be public, and you may not have a bot with your name on it, but it behooves you to act like there is one that tracks you at your job. As you consider how best to respond, there are three strategies you can try: Be secretive about what you do: My bot has learned how I think and what I do because everything I do is public - on my blog, on YouTube and in my recorded classes. I know that some of you may argue that I have facilitated my own disruption, and that being more secretive with my work would have kept my bot at bay. As a teacher, I neither want that secrecy, nor do I think it is feasible, but your work may lend itself better to this strategy. There are two reasons to be wary, though. The first is that if others do what you do, an AI entity can still imitate you, making it unlikely that you will escape unscathed. The second is that your actions may give away your methods and work process, and AI can thus reverse engineer what you do, and replicate it. Active investing, where portfolio managers claim to use secret sauces to find good investments, can be replicated at relatively low cost, if we can observe what these managers buy and sell. There is a good reason why ETFs have taken away market share from fund managers. Get system protection: I have bought and sold houses multiple times in my lifetime, and it is not only a process that is filled with intermediaries (lawyers, realtors, title deed checkers), all of whom get a slice from the deal, but one where you wonder what they all do in return for their fees. The answer often is not rooted in logic, but in the process, where the system (legal, real estate) requires these intermediaries to be there for the house ownership to transfer. This system protection for incumbents is not just restricted to real estate, and cuts across almost every aspect of our lives, and it creates barriers to disruption. Thus, even if AI can replicate what appraisers do, at close to no cost, I will wager that courts and accounting rule writers will be persuaded by the appraisal ecosystem that the only acceptable appraisals can come from human appraisers. Build your moat: In business, companies with large, sustainable competitive advantages are viewed as having moats that are difficult to competitors to breach, and are thus more valuable. That same idea applies at the personal level, especially as you look at the possibility of AI replacing you. It is your job, and mine, to think of the moats that we can erect (or already have) that will make it more difficult for our bots to replace us. As to what those moats might be, I cannot answer for you, but the last section lays out my thinking on what I need to do to stay a step ahead. Needless to say, I am a work in progress, even at this stage of my life, and rather than complain or worry about my bot replacing me, I will work on staying ahead. It is entirely possible that I am embarking on an impossible mission, but I will keep you posted on my progress (or absence of it). Of course, my bot can get so much better at what I do than I am, in which case, this blog may very well be written and maintained by it, and you will never know! YouTube Video Blog Posts (referenced) Investing and Valuation: Lessons from the Renaissance Stories and Numbers: How a number cruncher learned story telling! The Google Search Curse (my interview) Snowmen and Shovels: Lessons on Investing Catastrophic Risk: Investing and Business Implications
As I reveal my ignorance about TikTok trends, social media celebrities and Gen Z slang, my children are quick to point out my age, and I accept that reality, for the most part. I understand that I am too old to exercise without stretching first or eat a heaping plate of cheese fries and not suffer heartburn, but that does not stop me from trying occasionally. For the last decade or so, I have argued that businesses, like human beings, age, and struggle with aging, and that much of the dysfunction we observe in their decision making stems from refusing to act their age. In fact, the business life cycle has become an integral part of the corporate finance, valuation and investing classes that I teach, and in many of the posts that I have written on this blog. In 2022, I decided that I had hit critical mass, in terms of corporate life cycle content, and that the material could be organized as a book. While the writing for the book was largely done by November 2022, publishing does have a long lead time, and the book, published by Penguin Random House, will be available on August 20, 2024, at a book shop near you. If you are concerned that you are going to be hit with a sales pitch for that book, far from it! Rather than try to part you from your money, I thought I would give a compressed version of the book in this post, and for most of you, that will suffice. Setting the Stage The notion of a business life cycle is neither new nor original, since versions of it have floated around in management circles for decades, but its applications in finance have been spotty, with some attempts to tie where a company is in the life cycle to its corporate governance and others to accounting ratios. In fact, and this should come as no surprise to anyone who is familiar with his work, the most incisive piece tying excess returns (return on invested capital minus cost of capital) to the corporate life cycle was penned by Michael Mauboussin (with Dan Callahan) just a few months ago. My version of the corporate life cycle is built around six stages with the first stage being an idea business (a start-up) and the last one representing decline and demise. As you can see, the key tasks shift as business age, from building business models in the high growth phase to scaling up the business in high growth to defending against competition in the mature phase to managing decline int he last phase. Not surprisingly, the operating metrics change as companies age, with high revenue growth accompanied by big losses (from work-in-progress business models) and large reinvestment needs (to delivery future growth) in early-stage companies to large profits and free cash flows in the mature phase to stresses on growth and margins in decline. Consequently, in terms of cash flows, young companies burn through cash, with the burn increasing with potential, cash buildup is common as companies mature followed by cash return, as the realization kicks in that a company’s high growth days are in the past. As companies move through the life cycle, they will hit transition points in operations and in capital raising that have to be navigated, with high failure rates at each transition. Thus, most idea businesses never make it to the product phase, many product companies are unable to scale up, and quite a few scaled up firms are unable to defend their businesses from competitors. In short, the corporate life cycle has far higher mortality rates as businesses age than the human life cycle, making it imperative, if you are a business person, that you find the uncommon pathways to survive and grow. Measures and Determinants If you buy into the notion of a corporate life cycle, it stands to reason that you would like a way to determine where a company stands in the life cycle. There are three choices, each with pluses and minuses. The first is to focus on corporate age, where you estimate how old a company is, relative its founding date; it is easy to obtain, but companies age at different rates (as well will argue in the following section), making it a blunt weapon. The second is to look at the industry group or sector that a company is in, and then follow up by classifying that industry group or sector into high or low growth; for the last four decades, in US equity markets, tech has been viewed as growth and utilities as mature. Here again, the problem is that high growth industry groups begin to mature, just as companies do, and this has been true for some segments of the tech sector. The third is to focus on the operating metrics of the firm, with firms that deliver high revenue growth, with low/negative profits and negative free cash flows being treated as young firms. It is more data-intensive, since making a judgment on what comprises high (revenue growth or margins) requires estimating these metrics across all firms. While I delve into the details of all three measures, corporate age works surprisingly well as a proxy for where a company falls in the life cycle, as can be seen in this table of all publicly traded companies listed globally, broken down by corporate age into ten deciles: As you can see, the youngest companies have much higher revenue growth and more negative operating margins than older companies. Ultimately, the life cycles for companies can vary on three dimensions - length (how long a business lasts), height (how much it can scale up before it plateaus) and slope (how quickly it can scale up). Even a cursory glance at the companies that surround you should tell you that there are wide variations across companies, on these dimensions. To see why, consider the factors that determine these life cycle dimensions: Companies in capital-light businesses, where customers are willing to switch from the status quo, can scale up much faster than companies in capital-intensive businesses, where brand names and customer inertia can make breakthroughs more difficult. It is worth noting, though, that the forces that allow a business to scale up quickly often limit how long it can stay at the top and cause decline to be quicker, a trade off that was ignored during the last decade, where scaling up was given primacy. The drivers of the corporate life cycle can also explain why the typical twenty-first century company faces a compressed life cycle, relative to its twentieth century counterpart. In the manufacturing-centered twentieth century, it took decades for companies like GE and Ford to scale up, but they also stayed at the top for long periods, before declining over decades. The tech-centered economy that we live in is dominated by companies that can scale up quickly, but they have brief periods at the top and scale down just as fast. Yahoo! and BlackBerry soared from start ups to being worth tens of billions of dollars in a blink of an eye, had brief reigns at the top and melted down to nothing almost as quickly. Tech companies age in dog years, and the consequences for how we manage, value and invest in them are profound. In fact, I would argue that the lessons that we teach in business school and the processes that we use in analysis need adaptation for compressed life cycle companies, and while I don't have all the answers, the discussion about changing practices is a healthy one. Corporate Finance across the Life Cycle Corporate finance, as a discipline, lays out the first principles that govern how to run a business, and with a focus on maximizing value, all decisions that a business makes can be categorized into investing (deciding what assets/projects to invest in), financing (choosing a mix of debt and equity, as well as debt type) and dividend decisions (determining how much, if any, cash to return to owners, and in what form). While the first principles of corporate finance do not change as a company ages, the focus and estimation processes will shift, as shown in the picture below: With young companies, where the bulk of the value lies in future growth, and earnings and cash flows are often negative, it is the investment decision that dominates; these companies cannot afford to borrow or pay dividends. With more mature companies, as investment opportunities become scarcer, at least relative to available capital, the focus not surprisingly shifts to financing mix, with a lower hurdle rate being the pay off. With declining businesses, facing shrinking revenues and margins, it is cash return or dividend policy that moves into the front seat. Valuation across the Life Cycle I am fascinated by valuation, and the link between the value of a business and its fundamentals - cash flows, growth and risk. I am also a realist and recognize that I live in a world, where pricing dominates, with what you pay for a company or asset being determined by what others are paying for similar companies and assets: All companies can be both valued and priced, but the absence of history and high uncertainty about the future that characterizes young companies makes it more likely that pricing will dominate valuation more decisively than it does with more mature firms. All businesses, no matter where they stand in the life cycle, can be valued, but there are key differences that can be off putting to some. A well done valuation is a bridge between stories and numbers, with the interplay determining how defensible the valuation is, but the balance between stories and numbers will shift, as you move through the life cycle: With young companies, absent historical data on growth and profitability, it is your story for the company that will drive your numbers and value. As companies age, the numbers will become more important, as the stories you tell will be constrained by what you have been able to deliver in growth and margins. If your strength as an analyst or appraiser is in bounded story telling, you will be better served valuing young companies, whereas if you are a number-cruncher (comfortable with accounting ratios and elaborate spreadsheet models), you will find valuing mature companies to be your natural habitat. The draw of pricing is strong even for those who claim to be believers in value, and pricing in its simplest form requires a standardized price (a multiple like price earnings or enterprise value to EBITDA) and a peer group. While the pricing process is the same for all companies, the pricing metrics you use and the peer groups that you compare them to will shift as companies age: For pre-revenue and very young companies, the pricing metrics will standardize the price paid (by venture capitalists and other investors) to the number of users or subscribers that a company has or to the total market that its product is aimed at. As business models develop, and revenues come into play, you are likely to see a shift to revenue multiples, albeit often to estimated revenues in a future year (forward numbers). In the mature phase, you will see earnings multiples become more widely used, with equity versions (like PE) in peer groups where leverage is similar across companies, and enterprise value versions (EV to EBITDA) in peer groups, where leverage is different across companies. In decline, multiples of book value will become more common, with book value serving as a (poor) proxy for liquidation or break up value. In short, if you want to be open to investing in companies across the life cycle, it behooves you to become comfortable with different pricing ratios, since no one pricing multiple will work on all firms. Investing across the Life Cycle In my class (and book) on investment philosophies, I start by noting that every investment philosophy is rooted in a belief about markets making (and correcting) mistakes, and that there is no one best philosophy for all investors. I use the investment process, starting with asset allocation, moving to stock/asset selection and ending with execution to show the range of views that investors bring to the game: Market timing, whether it be based on charts/technical indicators or fundamentals, is primarily focused on the asset allocation phase of investing, with cheaper (based upon your market timing measures) asset classes being over weighted and more expensive asset classes being under weighted. Within the stock selection phase, there are a whole host of investment philosophies, often holding contradictory views of market behavior. Among stock traders, for instance, there are those who believe that markets learn slowly (and go with momentum) and those who believe that markets over react (and bet on reversals). On the investing side, you have the classic divide between value and growth investors, both claiming the high ground. I view the differences between these two groups through the prism of a financial balance sheet: Value investors believe that the best investment bargains are in mature companies, where assets in place (investments already made) are being underpriced by the market, whereas growth investors build their investment theses around the idea that it is growth assets where markets make mistakes. Finally, there are market players who try to make money from market frictions, by locking in market mispricing (with pure or near arbitrage). Drawing on the earlier discussion of value versus price, you can classify market players into investors (who value companies, and try to buy them at a lower price, while hoping that the gap closes) and traders (who make them money on the pricing game, buying at a low price and selling at a higher one). While investors and traders are part of the market in every company, you are likely to see the balance between the two groups shift as companies move through the life cycle: Early in the life cycle, it is undeniable that traders dominate, and for investors in these companies, even if they are right in their value assessments, winning will require much longer time horizons and stronger stomachs. As companies mature, you are likely to see more investors become part of the game, with bargain hunters entering when the stock drops too much and short sellers more willing to counter when it goes up too much. In decline, as legal and restructuring challenges mount, and a company can have multiple securities (convertibles, bonds, warrants) trading on it, hedge funds and activists become bigger players. In sum, the investment philosophy you choose can lead you to over invest in companies in some phases of the life cycle, and while that by itself is not a problem, denying that this skew exists can become one. Thus, deep value investing, where you buy stocks that trade at low multiples of earnings and book value, will result in larger portions of the portfolio being invested in mature and declining companies. That portfolio will have the benefit of stability, but expecting it to contain ten-baggers and hundred-baggers is a reach. In contrast, a venture capital portfolio, invested almost entirely in very young companies, will have a large number of wipeouts, but it can still outperform, if it has a few large winners. Advice on concentrating your portfolio and having a margin of safety, both value investing nostrums, may work with the former but not with the latter. Managing across the Life Cycle Management experts who teach at business schools and populate the premier consulting firms have much to gain by propagating the myth that there is a prototype for a great CEO. After all, it gives them a reason to charge nose-bleed prices for an MBA (to be imbued with these qualities) or for consulting advice, with the same end game. The truth is that there is no one-size-fits-all for a great CEO, since the qualities that you are looking for in top management will shift as companies age: Early in the life cycle, you want a visionary at the top, since you have to get investors, employees and potential customers to buy into that vision. To turn the vision into products and services, though, you need a pragmatist, willing to accept compromises. As the focus shifts to business models, it is the business-building skills that make for a great CEO, allowing for scaling up and success. As a scaled-up business, the skill sets change again, with opportunism becoming the key quality, allowing the company to find new markets to grow in. In maturity, where playing defense becomes central, you want a top manager who can guard a company's competitive advantages fiercely. Finally, in decline, you want CEOs, unencumbered by ego or the desire to build empires, who are willing to preside over a shrinking business, with divestitures and cash returns high on the to-do list. There are very few people who have all of these skills, and it should come as no surprise that there can be a mismatch between a company and its CEO, either because they (CEO and company) age at different rates or because of hiring mistakes. Those mismatches can be catastrophic, if a headstrong CEO pushes ahead with actions that are unsuited to the company he or she is in charge off, but they can be benign, if the mismatched CEO can find a partner who can fill in for weaknesses: While the possibilities of mismatches have always been part of business, the compression of corporate life cycles has made them both much more likely, as well as more damaging. After all, time took care of management transitions for long-lived twentieth century firms, but with firms that can scale up to become market cap giants in a decade, before scaling down and disappearing in the next one, you can very well see a founder/CEO go from being a hero in one phase to a zero in the next one. As we have allowed many of the most successful firms that have gone public in this century to skew the corporate finance game, with shares with different voting rights, we may be losing our power to change management at those firms where the need for change is greatest. Aging gracefully? The healthiest response to aging is acceptance, where a business accepts where it is in the life cycle, and behaves accordingly. Thus, a young firm that derives much of its value from future growth should not put that at risk by borrowing money or by buying back stock, just as a mature firm, where value comes from its existing assets and competitive advantages, should not risk that value by acquiring companies in new and unfamiliar businesses, in an attempt to return to its growth days. Acceptance is most difficult for declining firms, since the management and investors have to make peace with downsizing the firm. For these firms, it is worth emphasizing that acceptance does not imply passivity, a distorted and defeatist view of karma, where you do nothing in the face of decline, but requires actions that allow the firm to navigate the process with the least pain and most value to its stakeholders. It should come as no surprise that many firms, especially in decline, choose denial, where managers and investors come up with excuses for poor performance and lay blame on outside factors. On this path, declining firms will continue to act the way they did when they were mature or even growth companies, with large costs to everyone involved. When the promised turnaround does not ensue, desperation becomes the alternative path, with managers gambling large sums of other people’s money on long shots, with predictable results. The siren song that draws declining firms to make these attempts to recreate themselves, is the hope of a rebirth, and an ecosystem of bankers and consultants offers them magic potions (taking the form of proprietary acronyms that either restate the obvious or are built on foundations of made-up data) that will make them young again. They are aided and abetted by case studies of companies that found pathways to reincarnation (IBM in 1992, Apple in 2000 and Microsoft in 2013), with the added bonus that their CEOs were elevated to legendary status. While it is undeniable that companies do sometimes reincarnate, it is worth recognizing that they remain the exception rather than the rule, and while their top management deserves plaudits, luck played a key role as well. I am a skeptic on sustainability, at least as applied to companies, since its makes corporate survival the end game, sometimes with substantial costs for many stakeholders, as well as for society. Like the Egyptian Pharaohs who sought immortality by wrapping their bodies in bandages and being buried with their favorite possessions, companies that seek to live forever will become mummies (and sometimes zombies), sucking up resources that could be better used elsewhere. In conclusion It is the dream, in every discipline, to come up with a theory or construct that explains everything in that disciple. Unlike the physical sciences, where that search is constrained by the laws of nature, the social sciences reflect more trial and error, with the unpredictability of human nature being the wild card. In finance, a discipline that started as an offshoot of economics in the 1950s, that search began with theory-based models, with portfolio theory and the CAPM, veered into data-based constructs (proxy models, factor analysis), and behavioral finance, with its marriage of finance and psychology. I am grateful for those contributions, but the corporate life cycle has offered me a low-tech, but surprisingly wide reaching, construct to explain much of what I see in business and investment behavior. If you find yourself interested in the topic, you can try the book, and in the interests of making it accessible to a diverse reader base, I have tried to make it both modular and self-standing. Thus, if you are interested in how running a business changes, as it ages, you can focus on the four chapters that look at corporate finance implications, with the lead-in chapter providing you enough of a corporate finance foundation (even if you have never taken a corporate finance class) to be able to understand the investing, financing and dividend effects. If you are an appraiser or analyst, interested in valuing companies across the life cycle, it is the five chapters on valuation that may draw your interest, again with a lead-in chapter containing an introduction to valuation and pricing. As an investor, no matter what your investment philosophy, it is the four chapters on investing across the life cycle that may appeal to you the most. While I am sure that you will have no trouble finding the book, I have a list of book retailers listed below that you can use, if you choose, and the webpage supporting the book can be found here. If you are budget-constrained or just don't like reading (and there is no shame in that), I have also created an online class, with twenty sessions of 25-35 minutes apiece, that delivers the material from the book. It includes exercises that you can use to check your understanding, and the link to the class is here. YouTube Video Book and Class Webpages Book webpage: https://pages.stern.nyu.edu/~adamodar//New_Home_Page/CLC.htm Class webpage: https://pages.stern.nyu.edu/~adamodar//New_Home_Page/webcastCLC.htm YouTube Playlist for class: https://www.youtube.com/playlist?list=PLUkh9m2BorqlpbJBd26UEawPHk0k9y04_ Links to booksellers Amazon: https://www.amazon.com/Corporate-Lifecycle-Investment-Management-Implications/dp/0593545060 Barnes & Noble: https://www.barnesandnoble.com/w/the-corporate-life-cycle-aswath-damodaran/1143170651?ean=9780593545065 Bookshop.org: https://bookshop.org/p/books/the-corporate-lifecycle-business-investment-and-management-implications-aswath-damodaran/19850366?ean=9780593545065 Apple: https://books.apple.com/us/audiobook/the-corporate-life-cycle-business-investment/id1680865376 There is an Indian edition that will be released in September, which should be available in bookstores there. The Indian edition can be found on Amazon India. Amazon India: https://www.amazon.in/Corporate-Life-Cycle-Investment-Implications/dp/0143471392
After the 2008 market crisis, I resolved that I would be far more organized in my assessments and updating of equity risk premiums, in the United States and abroad, as I looked at the damage that can be inflicted on intrinsic value by significant shifts in risk premiums, i.e., my definition of a crisis. That precipitated my practice of estimating implied equity risk premiums for the S&P 500, at the start of every month, and following up of using those estimated premiums when valuing companies during that month. The 2008 crisis also gave rise to two risk premium papers that I have updated each year: the first looks at equity risk premiums, what they measure, how they vary across time and how best to estimate them, with the last update in March 2024. The second focuses on country risk and how it varies across geographies, with the focus again on determinants, measures and estimation, which I update mid-year each year. This post reflects my most recent update from July 2024 of country risk, and while you can read the entire paper here, I thought I would give you a mildly abridged version in this post. Country Risk: Determinants At the risk of stating the obvious, investing and operating in some countries is much riskier than investing and operating in others, with variations in risk on multiple dimensions. In the section below, I highlight the differences on four major dimensions - political structure, exposure to war/violence, extent of corruption and protections for legal and property rights, with the focus firmly on the economic risks rather than on social consequences. a. Political Structure Would you rather invest/operate in a democracy than in an autocracy? From a business risk perspective, I would argue that there is a trade off, sometimes making the former more risky than the latter, and sometimes less so. The nature of a democracy is that a government will be less able to promise or deliver long term predictable/stable tax and regulatory law, since losing an election can cause shifts in policy. Consequently, operating and investing in a democratic country will generally come with more risk on a continuous basis, with the risk increasing with partisanship in the country. Autocratic governments are in a better position to promise and deliver stable and predictable business environments, with two caveats. The first is that when change comes in autocracies, it will be both unexpected and large, with wrenching and discontinuous shifts in economic policy. The second is that the absence of checks and balance (legal, legislative, public opinion) will also mean that policy changes can be capricious, often driven by factors that have little to do with business or public welfare. Any attempt to measure political freedom comes with qualifiers, since the biases of the measuring service on what freedoms to elevate and which ones to ignore will play a role, but in the figure below, I report the Economist's Democracy Index, which is based upon five measures - electoral process and pluralism, government functioning, political participation, democratic social culture and civil liberties: Democracy Index in 2023: Source: The Economist Based upon the Economist's democracy measures, much of the world remains skewed towards authoritarianism, changing the risk exposures that investors and businesses face when operating in those parts of the world. b. War and Violence Operating a business becomes much more difficult, when surrounded by war and violence, from both within and outside the country. That difficulty also translates into higher costs, with those businesses that can buy protection or insurance doing so, and those that cannot suffering from damage and lost revenues. Drawing again on an external service, the Institute for Economics and Peace measures exposure to war and violence with a global peace index (with higher scores indicating more propensity towards violence): Global Peace Index 2024: Source: Institute for Economics & Peace While Africa and large swaths of Asia are exposed to violence, and Northern Europe and Canada remain peaceful, businesses in much of the world (including the United States) remain exposed to violence, at least according to this measure. c. Corruption As I have argued in prior posts, corruption operates as an implicit tax on businesses, with the tax revenues accruing to middlemen or third parties, rather than the government. Corruption Index 2023: Source: Transparency International Again, while you can argue with the scores and the rankings, it remains undeniable that businesses in much of the world face corruption (and its associated costs). While there are some who attribute it to culture, I believe that the overriding reasons for corruption are systems that are built around licensing and regulatory constraints, with poorly paid bureaucrats operating as the overseers There are other insidious consequences to corruption. First, as corruption becomes brazen, as it is in some parts of the world, there is evidence that companies operating in those settings are more likely to evade paying taxes to the government, thus redirecting tax revenues from the government to private players. Second, companies that are able and willing to play the corruption game will be put at an advantage over companies that are unable or unwilling to do so, creating a version of Gresham's law in businesses, where the least honorable businesses win out at the expense of the most honorable and honest ones. d. Legal and Property Rights When operating a business or making an investment, you are reliant on a legal system to back up your ownership rights, and to the extent that it does not do so, your business and investment will be worth less. The Property Rights Alliance, an entity that attempts to measure the strength of property rights, by country, measured property rights (physical and intellectual) around the world, to come up with a composite measure of these rights, with higher values translating into more rights. Their most recent update, from 2023, is captured in the picture below: Property Rights Index 2023: Source: The Property Rights Alliance Again, there are wide differences in property rights across the world; they are strongest in the North America and Europe and weakest in Africa and Latin America. Within each of these regions, though, there are variations across countries; within Latin America, Chile and Uruguay rank in the top quartile of countries with stronger property rights, but Venezuela and Bolivia are towards the bottom of the list. In assessing protections of property rights, it is worth noting that it is not only the laws that protect them that need to be looked at, but also the timeliness of legal action. A court that takes decades to act on violations of property rights is almost as bad as a court that does not enforce those rights at all. One manifestation of property right violation is nationalization, and here again there remain parts of the world, especially with natural resource businesses, where the risks of expropriation have increased. A Sustainalytics report that looked at metal miners documented 165 incidents of resources nationalization between 2017 and 2021, impacting 87 mining companies, with 22 extreme cases, where local governments ending contracts with foreign miners. Maplecroft, a risk management company, mapped out the trendline on nationalization risk in natural resources in the figure below: Source: Maplecroft National security is the reason that some governments use to justify public ownership of key resources. For instance, in 2022, Mexico created a state-owned company, Litio Para Mexico, to have a monopoly on lithium mining in the country, and announced a plan to renegotiate previously granted concessions to private companies to extract the resource. Country Risk: External factors Looking at the last section, you would not be faulted for believing that country risk exposure is self-determined, and that countries can become less risky by working on reducing corruption, increasing legal protections for property rights, making themselves safer and working on more predictable economic policies. That is true, but there are three factors that are largely out of their control that can still drive country risk upwards. 1. Commodity Dependence Some countries are dependent upon a specific commodity, product or service for their economic success. That dependence can create additional risk for investors and businesses, since a drop in the commodity’s price or demand for the product/service can create severe economic pain that spreads well beyond the companies immediately affected. Thus, if a country derives 50% of its economic output from iron ore, a drop in the price of iron ore will cause pain not only for mining companies but also for retailers, restaurants and consumer product companies in the country. The United Nations Conference on Trade and Development (UNCTAD) measures the degree to which a country is dependent on commodities, by looking at the percentage of its export revenues come from a commodities, and the figure below captures their findings: Proportion of revenues from commodities- 2019-2021; Source: UNCTAD Why don’t countries that derive a disproportionate amount of their economy from a single source diversify their economies? That is easier said than done, for two reasons. First, while it is feasible for larger countries like Brazil, India, and China to try to broaden their economic bases, it is much more difficult for small countries like Peru or Angola to do the same. Like small companies, these small countries have to find a niche where they can specialize, and by definition, niches will lead to over dependence upon one or a few sources. Second, and this is especially the case with natural resource dependent countries, the wealth that can be created by exploiting the natural resource will usually be far greater than using resources elsewhere in the economy, which may explain the inability of economies in the Middle East to wean itself away from oil. II. Life Cycle dynamics As readers of this blog should be aware, I am fond of using the corporate life cycle structure to explain why companies behave (or misbehave) and how investment philosophies vary. At the risk of pushing that structure to its limits, I believe that countries also go through a life cycle, with different challenges and risks at each stage: The link between life cycle and economic risk is worth emphasizing because it illustrates the limitations on the powers that countries have over their exposure to risk. A country that is still in the early stages of economic growth will generally have more risk exposure than a mature country, even if it is well governed and has a solid legal system. The old investment saying that gain usually comes with pain, also applies to operating and investing across the globe. While your risk averse side may lead you to direct your investments and operations to the safest parts of the world (say, Canada and Northern Europe), the highest growth is generally in the riskiest parts of the world. 3. Climate Change The globe is warming up, and no matter where you fall on the human versus nature debate, on causation, some countries are more exposed to global warming than others. That risk is not just to the health and wellbeing of those who live within the borders of these countries, but represents economic risks, manifesting as higher costs of maintaining day-to-day activity or less economic production. To measure climate change, we turned to ResourceWatch, a global partnership of public, private and civil society organizations convened by the World Resources Institute. This institute measure climate change exposure with a climate risk index (CRI), measuring the extent to which countries have been affected by extreme weather events (meteorological, hydrological, and climatological), and their most recent measures (from 2021, with an update expected late in 2024) of global exposure to climate risk is in the figure below: Climate Risk Index (CRI) in 2021: ResourceWatch Note that higher scores on the index indicate more exposure to country risk, and much of Africa, Latin America and Asia are exposed. In fact, since this map was last updated in 2021, it is conceivable that climate risk exposure has increased across the globe and that even the green regions are at risk of slipping away into dangerous territory. Country Life Cycle - Measures With that long lead in on the determinants of country risk, and the forces that can leave risk elevated, let us look at how best to measure country risk exposure. We will start with sovereign ratings, which are focused on country default risk, because they are the most widely used country risk proxies, before moving on to country risk scores, from public and private services, and closing with measures of risk premiums that equity investors in these countries should charge. 1. Sovereign Default Risk The ratings agencies that rate corporate bonds for default risk also rate countries, with sovereign ratings, with countries with higher (lower) perceived default risk receiving lower (higher) ratings. I know that ratings agencies are viewed with skepticism, and much of that skepticism is deserved, but it is undeniable that ratings and default risk are closely tied, especially over longer periods. The figure below summarizes sovereign ratings from Moody's in July 2024: Moody's Sovereign Ratings in July 2024; Source: Moody's If you compare these ratings to those that I reported in my last update, a year ago, you will notice that the ratings are stagnant for most countries, and when there is change, it is small. That remains my pet peeve with the rating agencies, which is not that they are biased or even wrong, but that they are slow to react to changes on the ground. For those searching for an alternative, there is the sovereign credit default swap (CDS) market, where you can market assessments of default risk. The figure below summarizes the spreads for the roughly 80 countries, where they are available: Sovereign CDS Spreads on June 30, 2024: Source: Bloomberg Sovereign CDS spreads reflect the pluses and minuses of a market-based measure, adjusting quickly to changes on the ground in a country, but sometimes overshooting as markets overreact. As you can see, the sovereign CDS market views India as safer than suggested by the ratings agencies, and for the first time, in my tracking, as safer than China (Sovereign CDS for India is 0.83% and for China is 1.05%, as of June 30, 2024). 2. Country Risk Scores Ubiquitous as sovereign ratings are, they represent a narrow measure of country risk, focused entirely on default risk. Thus, much of the Middle East looks safe, from a default risk perspective, but there are clearly political and economic risks that are not being captured. One antidote is to use a risk score that brings in these missed risks, and while there are many services that provide these scores, I use the ones supplied by Political Risk Services (PRS). PRS uses twenty two variables to measure country risk, whey then capture with a country risk score, from 0 to 100, with the riskiest countries having the lowest scores and the safest countries, the highest: Country risk scores in July 2024: Political Risk Services While I appreciate the effort that goes into these scores, I have issues with some of the scoring, as I am sure that you do. For instance, I find it incomprehensible that Libya and the United States share roughly the same PRS score, and that Saudi Arabia is safer than much of Europe. That said, I have tried other country risk scoring services (the Economist, The World Bank) and I find myself disagreeing with individual country scoring there as well. 3. Equity Risk Premiums Looking at operations and investing, through the eyes of equity investors, the risk that you care about is the equity risk premium, a composite measure that you then incorporate into expected returns. I don't claim to have prescience or even the best approach for estimating these equity risk premiums, but I have consistently followed the same approach for the last three decades. I start with the sovereign ratings, if available, and estimate default spreads based upon these ratings, and I then scale up these ratings for the fact that equities are riskier than government bonds. I then add these country risk premiums to my estimate of the implied equity risk premium for the S&P 500, to arrive at equity risk premiums, by country. For countries which have no sovereign ratings, I start with the country risk score from PRS for that country, find other (rated) countries with similar PRS scores, and extrapolate their ratings-based equity risk premiums. The final picture, at least as I see it in 2024, for equity risk premiums is below: Download spreadsheet You will undoubtedly disagree with the equity risk premiums that I attach to at least some of the countries on this list, and perhaps strongly disagree with my estimate for your native country, but you should perhaps take issue with Moody's or PRS, if that is so. Country Risk in Decision Making At this point, your reaction to this discussion might be "so what?", since you may see little use for these concepts in practice, either as a business or as an investor. In this section, I will argue that understanding equity risk premiums, and how they vary across geographies, can be critical in both business and personal investing. Country Risk in Business Most corporate finance classes and textbooks leave students with the proposition that the right hurdle rate to use in assessing business investments is the cost of capital, but create a host of confusion about what exactly that cost of capital measures. Contrary to popular wisdom, the cost of capital to use when assessing investment quality has little to do with the cost of raising financing for a company and more to do with coming up with an opportunity cost, i.e., a rate of return that the company can generate on investments of equivalent risk. Thus defined, you can see that the cost of capital that a company uses for an investment should reflect both the business risk as well as where in the world that investment is located. For a multinational consumer product company, such as Coca Cola, the cost of capital used to assess the quality of a Brazilian beverage project should be very different from the cost of capital estimated for a German beverage project, even if both are estimated in US dollars. The picture below captures the ingredients that go into a hurdle rate: Thus, in computing costs of equity and capital for its Brazil and German projects, Coca Cola will be drawing on the equity risk premiums for Brazil (7.87%) and Germany (4.11%), leading to higher hurdle rates for the former. The implications for multi-business, multi-national companies is that there is no one corporate cost of capital that can be used in assessing investments, since it will vary both across businesses and across geographies. A company in five businesses and ten geographies, with have fifty different costs of capital, and while you complaint may that this is too complicated, ignoring it and using one corporate cost of capital will lead you to cross subsidization, with the safest businesses and geographies subsidizing the riskiest. Country Risk in Investing As investors, we invest in companies, not projects, with those companies often having exposures in many countries. While it is possible to value a company in pieces, by valuing each its operations in each country, the absence of information at the country level often leads us to valuing the entire company, and when doing so, the risk exposure for that company comes from where it operates, not where it does business. Thus, when computing its cost of equity, you should look not only at its businesss risk, but what parts of the world it operates in: In intrinsic valuation, this will imply that a company with more of its operations in risky countries will be worth less than a company with equivalent earnings, growth and cash flows with operations in safer countries. Thus, rather than look at where a company is incorporated and traded, we should be looking at where it operates, both in terms of production and revenues; Nvidia is a company incorporated and traded in the United States, but as a chip designed almost entirely dependent on TSMC for its chip manufacture, it is exposed to China risk. It is true that most investors price companies, rather than value them, and use pricing metrics (PE ratios, EV to EBITDA) to judge cheap or expensive. If our assessment of country risk hold, we should expect to see variations in these pricing metrics across geographies. We computed EV to EBITDA multiples, based upon aggregate enterprise value and EBITDA, by country, in July 2024, and the results are captured in the figure below: Source: Raw data from S&P Capital IQ The results are mixed. While some of the riskiest parts of the world trade at low multiples of EBITDA, a significant part of Europe also does, including France and Norway. In fact, India trades at the highest multiple of EBITDA of any country in the world, representing how growth expectations can trump risk concerns. Currency Effects You may find it odd that I have spent so much of this post talking about country risk, without bringing up currencies, but that was not an oversight. It is true that riskier countries often have more volatile currencies that depreciate over time, but this more a symptom of country risk, than a cause. As I will argue in this section, currency choice affects your growth, cash flow and discount rate estimates, but ultimately should have no effect on intrinsic value. If you value a company in US dollars, rather than Indian rupees, should the numbers in your valuation be different? Of course, but the reason for the differences lies in the fact that different currencies bring different inflation expectations with them, and the key is to stay consistent: If expected inflation is lower in US dollars than in rupees, the cost of capital that you should obtain for a company in US dollars will be lower than the cost of capital in rupees, with the difference reflecting the expected inflation differential. However, since your cash flows will also then have to be in US dollars, the expected growth that you should use should reflect the lower inflation rate in dollars, and if you stay consistent in your inflation estimates, the effects should cancel out. This is not just theory, but common sense. Currency is a measurement mechanism, and to claim that a company is undervalued in one currency (say, the rupee) while claiming that it is overvalued at the same time in another currency (say, the US dollar) makes no sense. To practitioners who will counter with examples, where the value is different, when you switch currencies, my response is that there is a currency view (that the rupee is under or over priced relative to the dollar) in your valuation in your valuation, and that view should not be bundled together with your company story in a valuation. As we noted in the last section, the place that currency enters your valuation is in the riskfree rate, and if my assertion about expected inflation is right, variations in riskfree rates can be attributed entirely to difference in expected inflation. At the start of July 2024, for instance, I estimated the riskfree rates in every currency, using the US treasury bond rate as my dollar riskfree rate, and the differential inflation between the currency in question and the US dollar: My estimates are in the appendix to this post. In the same vein, inflation also enters into expected exchange rate calculations: This is, of course, the purchasing power parity theorem, and while currencies can deviate from this in the short term, it remains the best way to ensure that your currency views do not hijack your valuation. YouTube Video My Country risk premium paper Country Risk: Determinants, Measures and Implications - The 2024 Edition My Data Links Equity risk premiums, by country - July 2024 (also includes ratings, PRS scores and sovereign CDS) Riskfree rates in currencies, based upon differential inflation - July 2024 External Data Links Democracy Index 2023, The Economist Global Peace Index 2024: Source: Institute for Economics & Peace Corruption Index 2023, Transparency International International Property Rights Index 2023, Institute for Property Rights Climate Risk Index 2021, ResourceWatch
In the context of valuing companies, and sharing those valuations, I do get suggestions from readers on companies that I should value next. While I don't have the time or the bandwidth to value all of the suggested companies, a reader from Iceland, a couple of weeks ago, made a suggestion on a company to value that I found intriguing. He suggested Blue Lagoon, a well-regarded Icelandic Spa with a history of profitability, that was finding its existence under threat, as a result of volcanic activity in Southwest Iceland. In another story that made the rounds in recent weeks, 23andMe, a genetics testing company that offers its customers genetic and health information, based upon saliva sample, found itself facing the brink, after a hacker claimed to have hacked the site and accessed the genetic information of millions of its customers. Stepping back a bit, one claim that climate change advocates have made not just about fossil fuel companies, but about all businesses, is that investors are underestimating the effects that climate change will have on economic systems and on value. These are three very different stories, but what they share in common is a fear, imminent or expected, of a catastrophic event that may put a company's business at risk. Deconstructing Risk While we may use statistical measures like volatility or correlation to measure risk in practice, risk is not a statistical abstraction. Its impact is not just financial, but emotional and physical, and it predates markets. The risks that our ancestors faced, in the early stages of humanity, were physical, coming from natural disasters and predators, and physical risks remained the dominant form of risk that humans were exposed to, almost until the Middle Ages. In fact, the separation of risk into physical and financial risk took form just a few hundred years ago, when trade between Europe and Asia required ships to survive storms, disease and pirates to make it to their destinations; shipowners, ensconced in London and Lisbon, bore the financial risk, but the sailors bore the physical risk. It is no coincidence that the insurance business, as we know it, traces its history back to those days as well. I have no particular insights to offer on physical risk, other than to note that while taking on physical risks for some has become a leisure activity, I have no desire to climb Mount Everest or jump out of an aircraft. Much of the risk that I think about is related to risks that businesses face, how that risk affects their decision-making and how much it affects their value. If you start enumerating every risk a business is exposed to, you will find yourself being overwhelmed by that list, and it is for that reason that I categorize risk into the groupings that I described in an earlier post on risk. I want to focus in this post on the third distinction I drew on risk, where I grouped risk into discrete risk and continuous risk, with the later affecting businesses all the time and the former showing up infrequently, but often having much larger impact. Another, albeit closely related, distinction is between incremental risk, i.e., risk that can change earnings, growth, and thus value, by material amounts, and catastrophic risk, which is risk that can put a company's survival at risk, or alter its trajectory dramatically. There are a multitude of factors that can give rise to catastrophic risk, and it is worth highlighting them, and examining the variations that you will observe across different catastrophic risk. Put simply, a volcanic eruption, a global pandemic, a hack of a company's database and the death of a key CEO are all catastrophic events, but they differ on three dimensions: Source: I started this post with a mention of a volcano eruption in Iceland put an Icelandic business at risk, and natural disasters can still be a major factor determining the success or failure of businesses. It is true that there are insurance products available to protect against some of these risks, at least in some parts of the world, and that may allow companies in Florida (California) to live through the risks from hurricanes (earthquakes), albeit at a cost. Human beings add to nature's catastrophes with wars and terrorism wreaking havoc not just on human lives, but also on businesses that are in their crosshairs. As I noted in my post on country risk, it is difficult, and sometimes impossible, to build and preserve a business, when you operate in a part of the world where violence surrounds you. In some cases, a change in regulatory or tax law can put the business model for a company or many company at risk. I confess that the line between whether nature or man is to blame for some catastrophes is a gray one and to illustrate, consider the COVID crisis in 2020. Even if you believe you know the origins of COVID (a lab leak or a natural zoonotic spillover), it is undeniable that the choices made by governments and people exacerbated its consequences. Locus of Damage: Some catastrophes created limited damage, perhaps isolated to a single business, but others can create damage that extends across a sector geographies or the entire economy. The reason that the volcano eruptions in Iceland are not creating market tremors is because the damage is likely to be isolated to the businesses, like Blue Lagoon, in the path of the lava, and more generally to Iceland, an astonishingly beautiful country, but one with a small economic footprint. An earthquake in California will affect a far bigger swath of companies, partly because the state is home to the fifth largest economy in the world, and the pandemic in 2020 caused an economic shutdown that had consequences across all business, and was catastrophic for the hospitality and travel businesses. Likelihood: There is a third dimension on which catastrophic risks can vary, and that is in terms of likelihood of occurrence. Most catastrophic risks are low-probability events, but those low probabilities can become high likelihood events, with the passage of time. Going back to the stories that I started this post with, Iceland has always had volcanos, as have other parts of the world, and until recently, the likelihood that those volcanos would become active was low. In a similar vein, pandemics have always been with us, with a history of wreaking havoc, but in the last few decades, with the advance of medical science, we assumed that they would stay contained. In both cases, the probabilities shifted dramatically, and with it, the expected consequences. Business owners can try to insulate themselves from catastrophic risk, but as we will see in the next sections those protections may not exist, and even if they do, they may not be complete. In fact, as the probabilities of catastrophic risk increase, it will become more and more difficult to protect yourself against the risk. Dealing with catastrophic risk It is undeniable that catastrophic risk affects the values of businesses, and their market pricing, and it is worth examining how it plays out in each domain. I will start this section with what, at least for me, I is familiar ground, and look at how to incorporate the presence of catastrophic risk, when valuing businesses and markets. I will close the section by looking at the equally interesting question of how markets price catastrophic risk, and why pricing and value can diverge (again). Catastrophic Risk and Intrinsic Value Much as we like to dress up intrinsic value with models and inputs, the truth is that intrinsic valuation at its core is built around a simple proposition: the value of an asset or business is the present value of the expected cash flows on it: That equation gives rise to what I term the "It Proposition", which is that for "it" to have value, "it" has to affect either the expected cashflows or the risk of an asset or business. This simplistic proposition has served me well when looking at everything from the value of intangibles, as you can see in this post that I had on Birkenstock, to the emptiness at the heart of the claim that ESG is good for value, in this post. Using that framework to analyze catastrophic risk, in all of its forms, its effects can show in almost every input into intrinsic value: Looking at this picture, your first reaction might be confusion, since the practical question you will face when you value Blue Lagoon, in the face of a volcanic eruption, and 23andMe, after a data hack, is which of the different paths to incorporating catastrophic risks into value you should adopt. To address this, I created a flowchart that looks at catastrophic risk on two dimensions, with the first built around whether you can buy insurance or protection that insulates the company against its impact and the other around whether it is risk that is specific to a business or one that can spill over and affect many businesses. As you can see from this flowchart, your adjustments to intrinsic value, to reflect catastrophic risk will vary, depending upon the risk in question, whether it is insurable and whether it will affect one/few companies or many/all companies. A. Insurable Risk: Some catastrophic risks can be insured against, and even if firms choose not to avail themselves of that insurance, the presence of the insurance option can ease the intrinsic valuation process. Intrinsic Value Effect: If the catastrophic risk is fully insurable, as is sometimes the case, your intrinsic valuation became simpler, since all you have to do is bring in the insurance cost into your expenses, lowering income and cash flows, leave discount rates untouched, and let the valuation play out. Note that you can do this, even if the company does not actually buy the insurance, but you will need to find out the cost of that foregone insurance and incorporate it yourself. Pluses: Simplicity and specificity, because all this approach needs is a line item in the income statement (which will either exist already, if the company is buying insurance, or can be estimated). Minuses: You may not be able to insure against some risks, either because they are uncommon (and actuaries are unable to estimate probabilities well enough, to set premiums) or imminent (the likelihood of the event happening is so high, that the premiums become unaffordable). Thus, Blue Lagoon (the Icelandic spa that is threatened by a volcanic eruption) might have been able to buy insurance against volcanic eruption a few years ago, but will not be able to do so now, because the risk is imminent. Even when risks are insurable, there is a second potential problem. The insurance may pay off, in the event of the catastrophic event, but it may not offer complete protection. Thus, using Blue Lagoon again as an example, and assuming that the company had the foresight to buy insurance against volcanic eruptions a few years ago, all the insurance may do is rebuild the spa, but it will not compensate the company for lost revenues, as customers are scared away by the fear of volcanic eruptions. In short, while there are exceptions, much of insurance insures assets rather than cash flow streams. Applications: When valuing businesses in developed markets, we tend to assume that these businesses have insured themselves against most catastrophic risks and ignore them in valuation consequently. Thus, you see many small Florida-based resorts valued, with no consideration given to hurricanes that they will be exposed to, because you assume that they are fully insured. In the spirit of the “trust, but verity” proposition, you should probably check if that is true, and then follow up by examining how complete the insurance coverage is. 2. Uninsurable Risk, Going-concern, Company-specific: When a catastrophic risk is uninsurable, the follow up questions may lead us to decide that while the risk will do substantial damage, the injured firms will continue in existence. In addition, if the risk affects only one or a few firms, rather than wide swathes of the market, there are intrinsic value implications. Intrinsic Value Effect: If the catastrophic risk is not insurable, but the business will survive its occurrence even in a vastly diminished state, you should consider doing two going-concern valuations, one with the assumption that there is no catastrophe and one without, and then attaching a probability to the catastrophic event occurring. Expected Value with Catastrophe = Value without Catastrophe (1 – Probability of Catastrophe) + Value with Catastrophe (Probability of Catastrophe) In these intrinsic valuations, much of the change created by the catastrophe will be in the cash flows, with little or no change to costs of capital, at least in companies where investors are well diversified. Pluses: By separating the catastrophic risk scenario from the more benign outcomes, you make the problem more tractable, since trying to adjust expected cash flows and discount rates for widely divergent outcomes is difficult to do. Minuses: Estimating the probability of the catastrophe may require specific skills that you do not have, but consulting those who do have those skills can help, drawing on meteorologists for hurricane prediction and on seismologists for earthquakes. In addition, working through the effect on value of the business, if the catastrophe occurs, will stretch your estimation skills, but what options do you have? Applications: This approach comes into play for many different catastrophic risks that businesses face, including the loss of a key employee, in a personal-service business, and I used it in my post on valuing key persons in businesses. You can also use it to assess the effect on value of a loss of a big contract for a small company, where that contract accounts for a significant portion of total revenues. It can also be used to value a company whose business models is built upon the presence or absence of a regulation or law, in which case a change in that regulation or law can change value. 3. Uninsurable Risk. Failure Risk, Company-specific: When a risk is uninsurable and its manifestation can cause a company to fail, it poses a challenge for intrinsic value, which is, at its core, designed to value going concerns. Attempts to increase the discount rate, to bring in catastrophic risk, or applying an arbitrary discount on value almost never work. Intrinsic Value Effect: If the catastrophic risk is not insurable, and the business will not survive, if the risk unfolds, the approach parallels the previous one, with the difference being that that the failure value of the business, i.e, what you will generate in cash flows, if it fails, replaces the intrinsic valuation, with catastrophic risk built in: Expected Value with Catastrophe = Value without Catastrophe (1 – Probability of Catastrophe) + Failure Value (Probability of Catastrophe) The failure value will come from liquidation the assets, or what is left of them, after the catastrophe. Pluses: As with the previous approach, separating the going concern from the failure values can help in the estimation process. Trying to estimate cash flows, growth rates and cost of capital for a company across both scenarios (going concern and failure) is difficult to do, and it is easy to double count risk or miscount it. It is fanciful to assume that you can leave the expected cash flows as is, and then adjust the cost of capital upwards to reflect the default risk, because discount rates are blunt instruments, designed more to capture going-concern risk than failure risk. Minuses: As in the last approach, you still have to estimate a probability that a catastrophe will occur, and in addition, and there can be challenges in estimating the value of a business, if the company fails in the face of catastrophic risk. Applications: This is the approach that I use to value highly levered., cyclical or commodity companies, that can deliver solid operating and equity values in periods where they operate as going concerns, but face distress or bankruptcy, in the face of a severe recession. And for a business like the Blue Lagoon, it may be the only pathway left to estimate the value, with the volcano active, and erupting, and it may very well be true that the failure value can be zero. 4 & 5 Uninsurable Risk. Going Concern or Failure, Market or Sector wide: If a risk can affect many or most firms, it does have a secondary impact on the returns investors expect to make, pushing up costs of capital. Intrinsic Value Effect: The calculations for cashflows are identical to those done when the risks are company-specific, with cash flows estimated with and without the catastrophic risk, but since these risks are sector-wide or market-wide, there will also be an effect on discount rates. Investors will either see more relative risk (or beta) in these companies, if the risks affect an entire sector, or in equity risk premiums, if they are market-wide. Note that these higher discount rates apply in both scenarios. Pluses: The risk that is being built into costs of equity is the risk that cannot be diversified away and there are pathways to estimating changes in relative risk or equity risk premiums. Minuses: The conventional approaches to estimating betas, where you run a regression of past stock returns against the market, and equity risk premiums, where you trust in historical risk premiums and history, will not work at delivering the adjustments that you need to make. Applications: My argument for using implied equity risk premiums is that they are dynamic and forward-looking. Thus, during COVID, when the entire market was exposed to the economic effects of the pandemic, the implied ERP for the market jumped in the first six weeks of the pandemic, when the concerns about the after effects were greatest, and then subsided in the months after, as the fear waned: In a different vein, one reason that I compute betas by industry grouping, and update them every year, is in the hope that risks that cut across a sector show up as changes in the industry averages. In 2009, for instance, when banks were faced with significant regulatory changes brought about in response to the 2008 crisis, the average beta for banks jumped from 0.71 at the end of 2007 to 0.85 two years later. Catastrophic Risk and Pricing The intrinsic value approach assumes that we, as business owners and investors, look at catastrophic risk rationally, and make our assessments based upon how it will play out in cashflows, growth and risk. In truth, is worth remembering key insights from psychology, on how we, as human beings, deal with threats (financial and physical) that we view as existential. The first response is denial, an unwillingness to think about catastrophic risks. As someone who lives in a home close to one of California's big earthquake faults, and two blocks from the Pacific Ocean, I can attest to this response, and offer the defense that in its absence, I would wither away from anxiety and fear. The second is panic, when the catastrophic risk becomes imminent, where the response is to flee, leaving much of what you have behind. When looking at how the market prices in the expectation of a catstrophe occurring and its consequences, both these human emotions play out, as the overpricing of businesses that face catastrophic risk, when it is low probability and distant, and the underpricing of these same businesses when catastrophic risk looms large. To see this process at work, consider again how the market initially reacted to the COVID crisis in terms of repricing companies that were at the heart of the crisis. Between February 14, 2020 and March 23, 2020, when fear peaked, the sectors most exposed to the pandemic (hospitality, airlines) saw a decimation in their market prices, during that period: With catastrophic risk that are company-specific, you see the same phenomenon play out. The market capitalization of many young pharmaceutical company have been wiped out by the failure of blockbuster drug, in trials. PG&E, the utility company that provides power to large portions of California saw its stock price halved after wildfires swept through California, and investors worried about the culpability of the company in starting them. The most fascinating twist on how markets deal with risks that are existential is their pricing of fossil fuel companies over the last two decades, as concerns about climate change have taken center stage, with fossil fuels becoming the arch villain. The expectation that many impact investors had, at least early in this game, was that relentless pressure from regulators and backlash from consumers and investors would reduce the demand for oil, reducing the profitability and expected lives of fossil fuel companies. To examine whether markets reflect this view, I looked at the pricing of fossil fuel stocks in the aggregate, starting in 2000 and going through 2023: In the graph to the left, I chart out the total market value for all fossil fuel companies, and note a not unsurprising link to oil prices. In fact, the one surprise is that fossil fuel stocks did not see surges in market capitalization between 2011 and 2014, even as oil prices surged. While fossil fuel pricing multiples have gone up and down, I have computed the average on both in the 2000-2010 period and again in the 2011-2023 period. If the latter period is the one of enlightenment, at least on climate change, with warnings of climate change accompanied by trillions of dollars invested in combating it, it is striking how little impact it has had on how markets, and investors in the aggregate, view fossil fuel companies. In fact, there is evidence that the business pressure on fossil fuel companies has become less over time, with fossil fuel stocks rebounding in the last three years, and fossil fuel companies increasing investments and acquisitions in the fossil fuel space. Impact investors would point to this as evidence of the market being in denial, and they may be right, but market participants may point back at impact investing, and argue that the markets may be reflecting an unpleasant reality which is that despite all of the talk of climate change being an existential problem, we are just as dependent on fossil fuels today, as we were a decade or two decades ago: Don’t get me wrong! It is possible, perhaps even likely, that investors are not pricing in climate change not just in fossil fuel stocks, and that there is pain awaiting them down the road. It is also possible that at least in this case, that the market's assessment that doomsday is not imminent and that humanity will survive climate change, as it has other existential crises in the past. Mr. Market versus Mad Max Thunderdome The question posed about fossil fuel investors and whether they are pricing in the risks of gclimated change can be generalized to a whole host of other questions about investor behavior. Should buyers be paying hundreds of millions of dollars for a Manhattan office building, when all of New York may be underwater in a few decades? Lest I be accused of pointing fingers, what will happen to the value of my house that is currently two blocks from the beach, given the prediction of rising oceans. The painful truth is that if doomsday events (nuclear war, mega asteroid hitting the earth, the earth getting too hot for human existence) manifest, it is survival that becomes front and center, not how much money you have in your portfolio. Thus, ignoring Armageddon scenarios when valuing businesses and assets may be completely rational, and taking investors to task for not pricing assets correctly will do little to alter their trajectory! There is a lesson here for policy makers and advocates, which is that preaching that the planet is headed for the apocalypse, even if you believe it is true, will induce behavior that will make it more likely to happen, not less. On a different note, you probably know that I am deeply skeptical about sustainability, at least as preached from the Harvard Business School pulpit. It remains ill-defined, morphing into whatever its proponents want it to mean. The catastrophic risk discussion presents perhaps a version of sustainability that is defensible. To the extent that all businesses are exposed to catastrophic risks, some company-level and some having broader effects, there are actions that businesses can take to, if not protect to themselves, at least cushion the impact of these risks. A personal-service business, headed by an aging key person, will be well served designing a succession plan for someone to step in when the key person leaves (by his or her choice or an act of God). No global company was ready for COVID in 2020, but some were able to adapt much faster than others because they were built to be adaptable. Embedded in this discussion are also the limits to sustainability, since the notion of sustaining a business at any cost is absurd. Building in adaptability and safeguards against catastrophic risk makes sense only if the costs of doing so are less than the potential benefits, a simple but powerful lesson that many sustainability advocates seem to ignore, when they make grandiose prescriptions for what businesses should and should not do to avoid the apocalypse. YouTube
I was planning to finish my last two data updates for 2024, but decided to take a break and look at the seven stocks (Apple, Amazon, Alphabet, Meta, Microsoft, Nvidia and Tesla) which carried the market in 2023. While I will use the "Magnificent Seven" moniker attached by these companies by investors and the media, my preference would have been to call them the Seven Samurai. After all, like their namesakes in that legendary Kurosawa movie, who saved a village and its inhabitants from destruction, these seven stocks saved investors from having back-to-back disastrous years in the stock market. The What? It is worth remembering that the Magnificent Seven (Mag Seven) had their beginnings in the FANG (Facebook, Amazon, Netflix and Google) stocks, in the middle of the last decade, which morphed into the FANGAM (with the addition of Apple and Microsoft to the group) and then to the Mag Seven, with the removal of Netflix from the mix, and the addition of Tesla and Nvidia to the group. There is clearly hindsight bias in play here, since bringing in the best performing stocks of a period into a group can always create groups that have supernormal historical returns. That bias notwithstanding, these seven companies have been extraordinary investments, not just in 2023, but over the last decade, and there are lessons that we can learn from looking at the past. First, let's look at the performance of these seven stocks in 2023, when their collective market capitalization increased by a staggering $5.1 trillion during the course of the year. In a group of standout stocks, Nvidia and Meta were the best performers, with the former more than and the later almost tripling in value over the period. In terms of dollar value added, Microsoft and Apple each added a trillion dollars to their market capitalizations, during the year. To understand how much these stocks meant for overall market performance, recognize that these seven companies accounted for more than 50% of the increase in market capitalization of the the entire US equity market (which included 6658 listed companies in 2023). With them, US equities had price appreciation of 23.25% for the year, but without them, the year would have been an average one, with returns on 12.6%. While these seven stocks had an exceptional year in 2023, their outperformance stretches back for a much longer period. In the graph below, I look at the cumulated market capitalization of the Mag Seven stocks, and the market capitalization of all of the remaining US stocks from 2012 to 2023: Over the eleven-year period, the cumulative market capitalization of the seven companies has risen from $1.1 trillion in 2012 to $12 trillion in 2023, rising from 7.97% of overall US market cap in 2012 to 24.51% of overall market cap at the end of 2023. To put these numbers in perspective, the Mag Seven companies now have a market capitalization larger than that of all listed stocks in China, the second largest market in the world in market capitalization terms. Another way to see how much owning or not owning these stocks meant for investors, I estimated the cumulated value of $100 invested in December 2012 in a market-cap weighted index of US stocks at the end of 2023, first in US equities , and then in US equities, without the Mag Seven stocks: It is striking that removing seven stocks from a portfolio of 6658 US stocks, investing between 2012 and 2023, creates a 17.97% shortfall in the end value. In effect, this would suggest that any portfolio that did not include any of these seven stocks during the last decade would have faced a very steep, perhaps even insurmountable, climb to beat the market. That may go a long way in explaining why both value and small cap premium have essentially disappeared over this period. In all of the breathless coverage of the Mag Seven (and FANG and FANGAM) before it, there seems to be the implicit belief that their market dominance is unprecedented, but it is not. In fact, equity markets have almost always owed their success to their biggest winners, and Henrik Bessimbinder highlighted this reality by documenting that of the $47 trillion in increase in market capitalization between 1926 and 2019, five companies accounted for 22% of the increase in market value. I will wager that at the end of the next decade, looking back, we will find that a few companies accounted for the bulk of the rise in market capitalization during the decade, and another acronym will be created. The Why? When stocks soar as much as the Mag Seven stocks have in recent years, they evoke two responses. One is obviously regret on the part of those who did not partake in the rise, or sold too soon. The other is skepticism, and a sense that a correction is overdue, leading to what I call knee-jerk contrarianism, where your argument that these stocks are over priced is that they have gone up too much in the past. With these stocks, in particular, that reaction would have been costly over much of the last decade, since other than in 2022, these stocks have found ways to deliver positive surprises. In this section, we will look at the plausible explanations for the Mag Seven outperformance in 2023, starting with a correction/momentum story, where 2023 just represented a reversal of the losses in 2022, moving on to a profitability narrative, where the market performance of these companies can be related to superior profitability and operating performance, and concluding with an examination of whether the top-heavy performance (where a few large companies account for the bulk of market performance can explained by winner-take-all economics, 1. Correction/Momentum Story: One explanation for the Mag Seven's market performance in 2023 is that they were coming off a catastrophic year in 2022, where they collectively lost $4.8 trillion in market cap, and that 2023 represented a correction back to a level only slightly above the value at the end of 2021. There is some truth to this statement, but to see whether it alone can explain the Mag Seven 2023 performance, I broke all US stocks into deciles, based upon 2022 stock price performance, with the bottom decile including the stocks that went down the most in 2022 and the top decile the stocks that went up the most in 2022, and looked at returns in 2023: As you can see in the first comparison, the worst performing stocks in 2022 saw their market capitalizations increase by 35% in 2023, while the best performing stocks saw little change in market capitalization. Since all of the MAG 7 stocks fell into the bottom decile, I compared the performance of those stocks against the rest of the stocks in that decile, and th difference is start. While Mag Seven stocks saw their market capitalizations increase by 74%, the rest of the stocks in the bottom decile had only a 19% increase in market cap. In short, a portion of the Mag Seven stock performance in 2023 can be explained by a correction story, aided and abetted by strong momentum, but it is not the whole story. 2. Operating Performance/Profitability Narrative: While it is easy to attribute rising stock prices entirely to mood and momentum, the truth is that momentum has its roots in truth. Put differently, there are some good business reasons why the Mag Seven dominated markets in 2023: Pricing power and Economic Resilience: Coming into 2023, market and the Mag Seven stocks were battered, down sharply in 2022, largely because of rising inflation and concerns about an economic downturn. There were real concerns about whether the big tech companies that had dominated markets for the prior decade had pricing power and how well they would weather a recession. During the course of 2023, the Mag Seven set those fears to rest at least for the moment on both dimensions, increasing prices (with the exception of Tesla) on their products/services and delivering growth. In fact, if you are a Netflix subscriber or Amazon Prime member (and I would be surprised if any reader has neither, indicating their ubiquity), you saw prices increase on both services, and my guess is that you did not cancel your subscription/membership. With Alphabet and Meta, which make their money on online advertising, the rates for that advertising, measures in costs per click, rose through much of the year, and as an active Apple customer, I can guarantee that Apple has been passing through inflation into their prices all year. Money Machines: The pricing power and product demand resilience exhibited by these companies have manifested as strong earnings for the companies. In fact, both Alphabet and Meta have laid off thousands of employees, without denting revenues, and their profits in 2023 reflect the cost savings: Safety Buffers: As interest rates, for both governments and corporates, has risen sharply over the last two years, it is prudent for investors to worry about companies with large debt burdens, since old debt on the books, at low rates, will have to get refinanced at higher rates. With the Mag Seven, those concerns are on the back burner, because these companies have debt loads so low that they are almost non-existent. In fact, six of the seven firms in the Mag Seven grouping have cash balances that exceed their debt loads, giving them negative net debt levels. Put simply, there are good business reasons for why the seven companies in the Mag Seven have been elevated to superstar status. 3. Winner take all economics: It is undeniable that as the global economy has shifted away from its manufacturing base in the last century to a technology base, it has unleashed more "winner-take-all (or most" dynamics in many industries. In advertising, which was a splintered business where even the biggest players (newspapers, broadcasting companies) commanded small market shares of the overall market, Alphabet and Meta have acquired dominant market shares of online advertising, driven by easy scaling and network benefits (where advertising flows to the platforms with the most customers). Over the last two decades, Amazon has set in motion similar dynamics in retail and Microsoft's stranglehold on application and business software has been in existence even longer. In fact, it is the two newcomers into this group, Nvidia and Tesla, where questions remain about what the end game will look like, in terms of market share. Historically, neither the chip nor car businesses have been winner-take-all businesses, but investors are clearly pricing in the possibility that the changing economics of AI chips and electric cars could alter these businesses. This may seem like a cop out, but I think all three factors contributed to the success of the Mag Seven stocks in 2023. There was clearly a bounce back effect, as these firms recovered from a savage beatdown in 2022, but that bounce back occurred only because they were able to deliver strong profits and solid cash flows. And looking across the decade, I don't think it is debatable that investors have not only bought into the dominant player story (coming from the winner-take-all economics), but have also anointed these seven companies as leaders in the race to dominance in each of their businesses. The What Next? At the risk of stating the obvious, investing is always about the future, and a company's past market history, no matter how glorious, has little or no effect on whether it is a good investment today. I have long argued that investors need to separate what they think about the quality of a company (great, good or awful) from its quality as an investment (cheap or expensive). In fact, investing is about finding mismatches between what you think of a company and what investors have already priced in: I think that most of you will agree that the seven companies in the Mag Seven all qualify as very good to awesome, as businesses, and the last section provides backing, but the question that remains is whether our perceptions are shared by other investors, and already priced in. The tool that most investors use in making this assessment is pricing, and specifically, pricing multiples. In the table below, I compute pricing metrics for the Mag Seven, and compare them to that of the S&P 500: Trailing 12-month operating metrics used On every pricing metric, the Mag Seven stocks trade at a premium over the rest of the stocks in the S&P 500, and therein lies the weakest link in pricing. That premium can be justified by pointing to higher growth and margins at the Mag Seven stocks, but that is followed by a great deal of hand waving, since how much of a premium is up for grabs. Concocting growth-adjusted pricing multiples like PEG ratios is one solution, but the PEG ratio is an absolutely abysmal measuring of pricing, making assumptions about PE and growth that are untenable. The pricing game becomes even more unstable, when analysts replace current with forward earnings, with bias entering at every step. I know that some of you don't buy into intrinsic valuation and note quite correctly that there are lots of assumptions that you have to make about growth, profitability and risk to arrive at a value and that no matter how hard you try, you will be wrong. I agree, but I remain a believer that intrinsic valuation is the only tool that I have for assessing whether the market is incorporating what I see in a company (awful to awesome). I have valued every company in the Mag Seven multiple times over the last decade, and based my judgments on investing in these companies on a comparison of my value estimates and price. With the operating numbers (revenues, earnings) coming in for the 2023 calendar year, I have updated my valuations, and here are my summary estimates: InputAlphabetAmazonAppleMicrosoftMetaNvidiaTesla Expected CAGR Revenue (next 5 years)8.00%12.00%7.50%15.00%12.00%32.20%31.10% Target Operating Margin30.00%14.00%36.00%45.00%40.00%40.00%13.07% Cost of Capital8.84%8.60%8.64%9.23%8.83%8.84%9.17% Value per share$138.14$155.72$176.79$355.88$445.10$436.34$183.75 Price per share$145.00$169.15$188.00$405.49$456.08$680.00$185.07 % Under or Over Valued4.97%8.62%6.34%13.94%2.47%55.84%0.72% Internal Rate of Return8.41%7.85%7.89%8.06%8.53%7.18%9.16% Full Valuation (Excel)LinkLinkLinkLinkLinkLinkLink * NVidia and Tesla were valued as the sum of the valuations of their different businesses. The growth and margins reported are for the consolidated company. First, while all of the companies in the Mag Seven have values that exceed their prices, Tesla and Meta look close to fairly valued, at current prices, Alphabet, Apple and Amazon are within striking distance of value, and Microsoft and Nvidia look over valued, with the latter especially so. It may be coincidence, but these are the two companies that have benefited most directly from the AI buzz, and my findings of over valuation may just reflect my lack of imagination on how big AI can get as a business. Just to be clear, though, I have built in substantial value from AI in my valuation of Nvidia, and given Microsoft significantly higher growth because of it, but it is plausible that I have not done enough. If intrinsic value is not your cup of tea, you can look at the internal rates of return that you would earn on these companies, at current market prices, and with my expected cash flows. For perspective, the median cost of capital for a US company at the start of 2024 was 8.60%, and while only Tesla delivers an expected return higher than that number, the test, with the exception of Nvidia, are close. I own all seven of these companies, which may strike you as contradictory, but with the exception of Tesla that I bought just last week, my acquisitions of the other seven companies occurred well in the past, and reflected my judgments that they were undervalued (at the time). To the question of whether I should be selling, which would be consistent with my current assessment that these stocks are overvalued, I hesitate for three reasons: The first is that my assessments of value come with error, and for at least five of the companies, the price is well within my range of value. The second is that I will have to pay a capital gains tax that will amount to close to 30%, with state taxes included. The third is psychological, since selling everything or nothing would leave me with regrets either way. Last summer, when I valued Nvidia in this post, I found it over valued at a price of $450, and sold half my holdings, choosing to hold the other half. Now that the price has hit $680, I plan to repeat that process, and sell half of my remaining holdings. Conclusion As I noted at the start of this post, the benefit of hindsight allows us to pick the biggest winners in the market, bundle them together in a group and then argue that the market would be lost without them. That is true, but it is neither original nor unique to this market. The Mag Seven stocks have had a great run, but their pricing now reflects, in my view, the fact that they are great companies, with business models that deliver growth, at scale, with profitability. If you have never owned any of these companies, your portfolio will reflect that choice, and jumping on to the bandwagon now will not bring back lost gains. You should bide your time, since in my experience, even the very best companies deliver disappointments, and that markets over react to these disappointments, simply because expectations have been set so high. It is at those times that you will find that the price is right! YouTube Video Intrinsic Valuations Alphabet in February 2024 Amazon in February 2024 Apple in February 2024 Microsoft in February 2024 Meta in February 2024 NVidia in February 2024 Tesla in February 2024
In my last three posts, I looked at the macro (equity risk premiums, default spreads, risk free rates) and micro (company risk measures) that feed into the expected returns we demand on investments, and argued that these expected returns become hurdle rates for businesses, in the form of costs of equity and capital. Since businesses invest that capital in their operations, generally, and in individual projects (or assets), specifically, the big question is whether they generate enough in profits to meet these hurdle rate requirements. In this post, I start by looking at the end game for businesses, and how that choice plays out in investment rules for these businesses, and then examine how much businesses generated in profits in 2023, scaled to both revenues and invested capital. The End Game in Business If you start a business, what is your end game? Your answer to that question will determine not just how you approach running the business, but also the details of how you pick investments, choose a financing mix and decide how much to return to shareholders, as dividend or buybacks. While private businesses are often described as profit maximizers, the truth is that if they should be value maximizers. In fact, that objective of value maximization drives every aspect of the business, as can be seen in this big picture perspective in corporate finance: For some companies, especially mature ones, value and profit maximization may converge, but for most, they will not. Thus, a company with growth potential may be willing to generate less in profits now, or even make losses, to advance its growth prospects. In fact, the biggest critique of the companies that have emerged in this century, many in social media, tech and green energy, is that they have prioritized scaling up and growth so much that they have failed to pay enough attention to their business models and profitability. For decades, the notion of maximizing value has been central to corporate finance, though there have been disagreements about whether maximizing stock prices would get you the same outcome, since that latter requires assumptions about market efficiency. In the last two decades, though, there are many who have argued that maximizing value and stockholder wealth is far too narrow an objective, for businesses, because it puts shareholders ahead of the other stakeholders in enterprises: It is the belief that stockholder wealth maximization shortchanges other stakeholders that has given rise to stakeholder wealth maximization, a misguided concept where the end game for businesses is redefined to maximize the interests of all stakeholders. In addition to being impractical, it misses the fact that shareholders are given primacy in businesses because they are the only claim holders that have no contractual claims against the business, accepting residual cash flows, If stakeholder wealth maximization is allowed to play out, it will result in confused corporatism, good for top managers who use stakeholder interests to become accountable to none of the stakeholders: As you can see, I am not a fan of confused corporatism, arguing that giving a business multiple objectives will mangle decision making, leaving businesses looking like government companies and universities, wasteful entities unsure about their missions. In fact, it is that skepticism that has made me a critic of ESG and sustainability, offshoots of stakeholder wealth maximization, suffering from all of its faults, with greed and messy scoring making them worse. It may seem odd to you that I am spending so much time defending the centrality of profitability to a business, but it is a sign of how distorted this discussion has become that it is even necessary. In fact, you may find my full-throated defense of generating profits and creating value to be distasteful, but if you are an advocate for the point of view that businesses have broader social purposes, the reality is that for businesses to do good, they have to be financial healthy and profitable. Consequently, you should be just as interested, as I am, in the profitability of companies around the world, albeit for different reasons. My interest is in judging them on their capacity to generate value, and yours would be to see if they are generating enough as surplus so that they can do good for the world. Profitability: Measures and Scalars Measuring profitability at a business is messier than you may think, since it is not just enough for a business to make money, but it has to make enough money to justify the capital invested in it. The first step is understanding profitability is recognizing that there are multiple measures of profit, and that each measure they captures a different aspect of a business: It is worth emphasizing that these profit numbers reflect two influences, both of which can skew the numbers. The first is the explicit role of accountants in measuring profits implies that inconsistent accounting rules will lead to profits being systematically mis-measured, a point I have made in my posts on how R&D is routinely mis-categorized by accountants. The other is the implicit effect of tax laws, since taxes are based upon earnings, creating an incentive to understate earnings or even report losses, on the part of some businesses. That said, global (US) companies collectively generated $5.3 trillion ($1.8 trillion) in net income in 2023, and the pie charts below provide the sector breakdowns for global and US companies: Notwithstanding their trials and tribulations since 2008, financial service firms (banks, insurance companies, investment banks and brokerage firms) account for the largest slice of the income pie, for both US and global companies, with energy and technology next on the list. Profit Margins While aggregate income earned is an important number, it is an inadequate measure of profitability, especially when comparisons across firms, when it is not scaled to something that companies share. As as a first scalar, I look at profits, relative to revenues, which yields margins, with multiple measures, depending upon the profit measure used: Looking across US and global companies, broken down by sector, I look at profit margins in 2023: Note that financial service companies are conspicuously absent from the margin list, for a simple reason. Most financial service firms have no revenues, though they have their analogs - loans for banks, insurance premiums for insurance companies etc. Among the sectors, energy stands out, generating the highest margins globally, and the second highest, after technology firms in the United States. Before the sector gets targeted as being excessively profitable, it is also one that is subject to volatility, caused by swings in oil prices; in 2020, the sector was the worst performing on profitability, as oil prices plummeted that year. Does profitability vary across the globe? To answer that question, I look at differences in margins across sub-regions of the world: You may be surprised to see Eastern European and Russian companies with the highest margins in the world, but that can be explained by two phenomena. The first is the preponderance of natural resource companies in this region, and energy companies had a profitable year in 2023. The second is that the sanctions imposed after 2021 on doing business in Russia drove foreign competitors out of the market, leaving the market almost entirely to domestic companies. At the other end of the spectrum, Chinese and Southeast Asian companies have the lowest net margins, highlighting the reality that big markets are not always profitable ones. Finally, there is a relationship between corporate age and profitability, with younger companies often struggling more to deliver profits, with business models still in flux and no economies of scale. In the fact, the pathway of a company through the life cycle can be seen through the lens of profit margins: Early in the life cycle, the focus will be on gross margins, partly because there are losses on almost every other earnings measure. As companies enter growth, the focus will shift to operating margins, albeit before taxes, as companies still are sheltered from paying taxes by past losses. In maturity, with debt entering the financing mix, net margins become good measures of profitability, and in decline, as earnings decline and capital expenditures ease, EBITDA margins dominate. In the table below, I look at global companies, broken down into decals, based upon corporate age, and compute profit margins across the deciles: The youngest companies hold their own on gross and EBITDA margins, but they drop off as you move to operating nnd net margins. In summary, profit margins are a useful measure of profitability, but they vary across sectors for many reasons, and you can have great companies with low margins and below-average companies that have higher margins. Costco has sub-par operating margins, barely hitting 5%, but makes up for it with high sales volume, whereas there are luxury retailers with two or three times higher margins that struggle to create value. Return on Investment The second scalar for profits is the capital invested in the assets that generate these profits. Here again, there are two paths to measuring returns on investment, and the best way to differentiate them is to think of them in the context of a financial balance sheet: The accounting return on equity is computed by dividing the net income, the equity investor's income measure, by the book value of equity and the return on invested capital is computed, relative to the book value of invested capital, the cumulative values of book values of equity and debt, with cash netted out. Looking at accounting returns, broken down by sector, for US and global companies, here is what 2023 delivered: In both the US and globally, technology companies deliver the highest accounting returns, but these returns are skewed by the accounting inconsistencies in capitalizing R&D expenses. While I partially correct for this by capitalizing R&D expenses, it is only a partial correction, and the returns are still overstated. The worst accounting returns are delivered by real estate companies, though they too are skewed by tax considerations, with expensing to reduce taxes paid, rather than getting earnings right. Excess Returns In the final assessment, I bring together the costs of equity and capital estimated in the last post and the accounting returns in this one, to answer a critical question that every business faces, i.e,, whether the returns earned on its investment exceed its hurdle rate. As with the measurement of returns, excess returns require consistent comparisons, with accounting returns on equity compared to costs of equity, and returns on capital to costs of capital: These excess returns are not perfect or precise, by any stretch of the imagination, with mistakes made in assessing risk parameters (betas and ratings) causing errors in the cost of capital and accounting choices and inconsistencies affecting accounting returns. That said, they remain noisy estimates of a company's competitive advantages and moats, with strong moats going with positive excess returns, no moats translating into excess returns close to zero and bad businesses generating negative excess returns. I start again by looking at the sector breakdown, both US and global, of excess returns in 2023, in the table below: In computing excess returns, I did add a qualifier, which is that I would do the comparison only among money making companies; after all, money losing companies will have accounting returns that are negative and less than hurdle rates. With each sector, to assess profitability, you have to look at the percentage of companies that make money and then at the percent of these money making firms that earn more than the hurdle rate. With financial service firms, where only the return on equity is meaningful, 57% (64%) of US (global) firms have positive net income, and of these firms, 82% (60%) generated returns on equity that exceeded their cost of equity. In contrast, with health care firms, only 13% (35%) of US (global) firms have positive net income, and about 68% (53%) of these firms earn returns on equity that exceed the cost of equity. In a final cut, I looked at excess returns by region of the world, again looking at only money-making companies in each region: To assess the profitability of companies in each region, I again look at t the percent of companies that are money-making, and then at the percent of these money-making companies that generate accounting returns that exceed the cost of capital. To provide an example, 82% of Japanese companies make money, the highest percentage of money-makers in the world, but only 40% of these money-making companies earn returns that exceed the hurdle rate, second only to China on that statistic. The US has the highest percentage (73%) of money-making companies that generate returns on equity that exceed their hurdle rates, but only 37% of US companies have positive net income. Australian and Canadian companies stand out again, in terms of percentages of companies that are money losers, and out of curiosity, I did take a closer look at the individual companies in these markets. It turns out that the money-losing is endemic among smaller publicly traded companies in these markets, with many operating in materials and mining, and the losses reflect both company health and life cycle, as well as the tax code (which allows generous depreciation of assets). In fact, the largest companies in Australia and Canada deliver enough profits to carry the aggregated accounting returns (estimated by dividing the total earnings across all companies by the total invested capital) to respectable levels. In the most sobering statistic, if you aggregate money-losers with the companies that earn less than their hurdle rates, as you should, there is not a single sector or region of the world, where a majority of firms earn more than their hurdle rates. In 2023, close to 80% of all firms globally earned returns on capital that lagged their costs of capital. Creating value is clearly far more difficult in practice than on paper or in case studies! A Wrap! I started this post by talking about the end game in business, arguing for profitability as a starting point and value as the end goal. The critics of that view, who want to expand the end game to include more stakeholders and a broader mission (ESG, Sustainability) seem to be operating on the presumption that shareholders are getting a much larger slice of the pie than they deserve. That may be true, if you look at the biggest winners in the economy and markets, but in the aggregate, the game of business has only become harder to play over time, as globalization has left companies scrabbling to earn their costs of capital. In fact, a decade of low interest rates and inflation have only made things worse, by making risk capital accessible to young companies, eager to disrupt the status quo. YouTube Video Datasets Profit Margins, by Industry (US, Global) Accounting Returns and Excess Returns, by Industry (US, Global) Data Update Posts for 2024 Data Update 1 for 2024: The Data Speaks, but what is it saying? Data Update 2 for 2024: A Stock Comeback - Winning the Expectations Game! Data Update 3 for 2024: Interest Rates in 2023 - A Rule-breaking Year Data Update 4 for 2024: Danger and Opportunity - Bringing Risk into the Equation Data Update 5 for 2024: Profitability - The End Game for Business?
In my last data updates for this year, I looked first at how equity markets rebounded in 2023, driven by a stronger-than-expected economy and inflation coming down, and then at how interest rates mirrored this rebound. In this post, I look at risk, a central theme in finance and investing, but one that is surprisingly misunderstood and misconstrued. In particular, there are wide variations in how risk is measured, and once measured, across companies and countries, and those variations can lead to differences in expected returns and hurdle rates, central to both corporate finance and investing judgments. Risk Measures There is almost no conversation or discussion that you can have about business or investing, where risk is not a part of that discussion. That said, and notwithstanding decades of research and debate on the topic, there are still wide differences in how risk is defined and measured. What is risk? I do believe that, in finance, we have significant advances in understanding what risk, I also think that as a discipline, finance has missed the mark on risk, in three ways. First, it has put too much emphasis on market-price driven measures of risk, where price volatility has become the default measure of risk, in spite of evidence indicating that a great deal of this volatility has nothing to do with fundamentals. Second, in our zeal to measure risk with numbers, we have lost sight of the reality that the effects of risk are as much on human psyche, as they are on economics. Third, by making investing a choice between good (higher returns) and bad (higher risk), a message is sent, perhaps unwittingly, that risk is something to be avoided or hedged. It is perhaps to counter all of these that I start my session on risk with the Chinese symbol for crisis: Chinese symbol for crisis = 危機 = Danger + Opportunity I have been taken to task for using this symbol by native Chinese speakers pointing out mistakes in my symbols (and I have corrected them multiple times in response), but thinking of risk as a combination of danger and opportunity is, in my view, a perfect pairing, and this perspective offers two benefits. First, by linking the two at the hip, it sends the clear and very important signal that you cannot have one (opportunity), without exposing yourself to the other (danger), and that understanding alone would immunize individuals from financial scams that offer the best of both worlds - high returns with no risk. Second, it removes the negativity associated to risk, and brings home the truth that you build a great business, not by avoiding danger (risk), but by seeking out the right risks (where you have an advantage), and getting more than your share of opportunities. Breaking down risk One reason that we have trouble wrapping our heads around risk is that it has so many sources, and our capacity to deal with varies, as a consequence. When assessing risk in a project or a company, I find it useful to make a list of every risk that I see in the investment, big and small, but I then classify these risks into buckets, based upon type, with very different ways of dealing with and incorporating that risk into investment analysis. The table below provides a breakdown of those buckets, with economic uncertainty contrasted with estimation uncertainty, micro risk separated from macro risks and discrete risks distinguished from continuous risks: While risk breakdowns may seem like an abstraction, they do open the door to healthier practices in risk analysis, including the following: Know when to stop: In a world, where data is plentiful and analytical tools are accessible, it is easy to put off a decision or a final analysis, with the excuse that you need to collect more information. That is understandable, but digger deeper into the data and doing more analysis will lead to better estimates, only if the risk that you are looking at is estimation risk. In my experience, much of the risk that we face when valuing companies or analyzing investments is economic uncertainty, impervious to more data and analysis. It is therefore healthy to know when to stop researching, accepting that your analysis is always a work-in-progress and that decisions have to be made in the face of uncertainty. Don't overthink the discount rate: One of my contentions of discount rates is that they cannot become receptacles for all your hopes and fears. Analysts often try to bring company-specific components, i.e, micro uncertainties, into discount rates, and in the process, they end up incorporating risk that investors can eliminate, often at no cost. Separating the risks that do affect discount rates from the risks that do not, make the discount rate estimation simpler and more precise. Use more probabilistic & statistical tools: The best tools for bringing in discrete risk are probabilistic, i.e., decision trees and scenario analysis, and using them in that context may open the door to other statistical tools, many of which are tailor-made for the problems that we face routinely in finance, and are underutilized. Measuring risk The financial thinking on risk, at least in its current form, had its origins in the 1950s, when Harry Markowitz uncovered the simple truth that the risk of an investment is not the risk of it standing alone, but the risk it adds to an investor's portfolio. He followed up by showing that holding diversified portfolios can deliver much higher returns, for given levels of risk, for all investors. That insight gave rise not only to modern portfolio theory, but it also laid the foundations for how we measure and deal with risk in finance. In fact, almost every risk and return model in finance is built on pairing two assumptions, the first being that the marginal investors in a company or business are diversified and the second being that investors convey their risk concerns through market prices: By building on the assumptions that the investors pricing a business are diversified, and make prices capture that risk, modern portfolio theory has exposed itself to criticism from those who disagree with one or both of these assumptions. Thus, there are value investors, whose primary disagreement is on the use of pricing measures for risk, arguing that risk has to come from numbers that drive intrinsic value - earnings and cash flows. There are other investors who are at peace with price-based risk measures , but disagree with the "diversified marginal investor" assumption, and they are more intent on finding risk measures that incorporate total risk, not just risk that cannot be diversified away. I do believe that the critiques of both groups have legitimate basis, and while I don't feel as strongly as they do, I can offer modifications of risk measures to counter the critiques; For investors who do not trust market prices, you can create risk analogs that look at accounting earnings or cash flows, and for those who believe that the diversified investor assumption is an overreach, you can adapt risk measures to capture all risk, not just market risk. In short, if you don't like betas and have disdain for modern portfolio theory, your choice should not be to abandon risk measurement all together, but to come up with an alternative risk measure that is more in sync with your view of the world. Risk Differences across Companies With that long lead-in on risk, we are positioned to take a look at how risk played out, at the company level, in 2024. Using the construct from the last section, I will start by looking at price-based risk measures and then move on to intrinsic risk measures in the second section. a. Price-based Risk Measures My data universe includes all publicly traded companies, and since they are publicly traded, computing price-based risk measures is straight forward. That said, it should be noted that liquidity varies widely across these companies, with some located in markets where trading is rare and others in markets, with huge trading volumes. With that caveat in mind, I computed three risk-based measures - a simplistic measure of range, where I look at the distance between the high and low prices, and scale it to the mid-point, the standard deviation in stock prices, a conventional measure of volatility and beta, a measure of that portion of a company's risk that is market-driven. I use the data through the end of 2023 to compute all three measures for every company, and in my first breakdown, I look at these risk measures, by sector (globally): Utilities are the safest or close to the safest , on all three price-based measures, but there are divergences on the other risk measures. Technology companies have the highest betas, but health care has the riskiest companies, on standard deviation and the price range measure. Looking across geographies, you can see the variations in price-based risk measures across the world: There are two effects at play here. The first is liquidity, with markets with less trading and liquidity exhibiting low price-based risk scores across the board. The second is that some geographies have sector concentrations that affect their pricing risk scores; the preponderance of natural resource and mining companies in Australia and Canada, for instance, explain the high standard deviations in 2023. Finally, I brought in my corporate life cycle perspective to the risk question, and looked at price-based risk measures by corporate age, with the youngest companies in the first decile and the oldest ones in the top decile (with a separate grouping for companies that don't have a founding year in the database): On both the price range and standard deviation measures, not surprisingly, younger firms are riskier than older ones, but on the beta measure, there is no relationship. That may sound like a contradiction, but it does reflect the divide between measures of total risk (like the price range and standard deviation) and measures of just market risk (like the beta). Much of the risk in young companies is company-specific, and for those investors who hold concentrated portfolios of these companies, that risk will translate into higher risk-adjusted required returns, but for investors who hold broader and more diversified portfolios, younger companies are similar to older companies, in terms of risk. b. Intrinsic Risk Measures As you can see in the last section, price-based risk measures have their advantages, including being constantly updated, but they do have their limits, especially when liquidity is low or when market prices are not trustworthy. In this section, I will look at three measures of intrinsic risk - whether a company is making or losing money, with the latter being riskier, the variability in earnings, with less stable earnings translating to higher risk, and the debt load of companies, with more debt and debt charges conferring more risk on companies. I begin by computing these intrinsic risk measures across sectors, with the coefficient of variation on both net income and operating income standing in for earnings variability; the coefficient of variation is computed by dividing the standard deviation in earnings over the last ten years, divided by the average earnings over those ten years. Globally, health care has the highest percentage of money-losing companies and utilities have the lowest. In 2023, energy companies have the most volatile earnings (net income and operating income) and real estate companies have the most onerous debt loads. Looking at the intrinsic risk measures for sub-regions across the world, here is what I see: Again, Australia and Canada have the highest percentage of money losing companies in the world and Japan has the lowest, Indian companies have the highest earnings variability and Chinese companies carry the largest debt load, in terms of debt as a multiple of EBITDA. In the last table, I look at the intrinsic risk measures, broken down by company age: Not surprisingly, there are more money losing young companies than older ones, and these young companies also have more volatile earnings. On debt load, though, there is no discernible pattern in debt load across age deciles, though the youngest companies do have the lowest interest coverage ratios (and thus are exposed to the most danger, if earnings drop). Risk Differences across Countries In this final section, I will look risk differences across countries, both in terms of why risk varies across, as well as how these variations play out as equity risk premiums. There are many reasons why risk exposures vary across countries, but I have tried to capture them all in the picture below (which I have used before in my country risk posts and in my paper on country risk): Put simply, there are four broad groups of risks that lead to divergent country risk exposures; political structure, which can cause public policy volatility, corruption, which operates as an unofficial tax on income, war and violence, which can create physical risks that have economic consequences and protections for legal and property rights, without which businesses quickly lose value. While it is easy to understand why risk varies across countries, it is more difficult to measure that risk, and even more so, to convert those risk differences into risk premiums. Ratings agencies like Moody's and S&P provide a measure of the default risk in countries with sovereign ratings, and I build on those ratings to estimate country and equity risk premiums, by country. The figure below summarizes the numbers used to compute these numbers at the start of 2024: The starting point for estimating equity risk premiums, for all of the countries, is the implied equity risk premium of 4.60% that I computed at the start of 2024, and talked about in my second data post this year. All countries that are rated Aaa (Moody's) are assigned 4.60% as equity risk premiums, but for lower-rated countries, there is an additional premium, reflecting their higher risk: Download data You will notice that there are countries, like North Korea, Russia and Syria, that are unrated but still have equity risk premiums, and for these countries, the equity risk premiums estimate is based upon a country risk score from Political Risk Services. If you are interested, you can review the process that I use in far more detail in this paper that I update every year on country risk. Risk and Investing The discussion in the last few posts, starting with equity risk premium in my second data update, and interest rates and default spreads in my third data update, leading into risk measures that differrentiate across companies and countries in this one, all lead in to a final computation of the costs of equity and capital for companies. That may sound like a corporate finance abstraction, but the cost of capital is a pivotal number that can alter whether and how much companies invest, as well as in what they invest, how they fund their investments (debt or equity) and how much they return to owners as dividends or buybacks. For investors looking at these companies, it becomes a number that they use to estimate intrinsic values and make judgments on whether to buy or sell stocks: The multiple uses for the cost of capital are what led me to label it "the Swiss Army knife of finance" and if you are interested, you can keep a get a deeper assessment by reading this paper. Using the updated numbers for the risk free rate (in US dollars), the equity risk premiums (for the US and the rest of the world) and the default spreads for debt in different ratings classes, I computed the cost of capital for the 47,698 companies in my data universe, at the start of 2024. In the graph below, I provide a distribution of corporate costs of capital, for US and global companies, in US dollars: If your frame of reference is another currency, be it the Euro or the Indian rupee, adding the differential inflation to these numbers will give you the ranges in that currency. At the start of 2024, the median cost of capital, in US dollars, is 7.9% (8.7%) for a US (global) company, lower than the 9.6 (10.6%) at the start of 2023, for US (global) stocks, entirely because of declines in the price of risk (equity risk premiums and default spreads), but the 2024 costs of capital are higher than the historic lows of 5.8% (6.3%) for US (Global) stocks at the start of 2022. In short, if you are a company or an investor who works with fixed hurdle rates over time, you may be using a rationale that you are just normalizing, but you have about as much chance of being right as a broken clock. What's coming? Since this post has been about risk, it is a given that things will change over the course of the year. If your question is how you prepare for that change, one answer is to be dynamic and adaptable, not only reworking hurdle rates as you go through the year, but also building in escape hatches and reversibility even into long term decisions. In case things don't go the way you expected them to, and you feel the urge to complain about uncertainty, I urge you to revisit the Chinese symbol for risk. We live in dangerous times, but embedded in those dangers are opportunities. If you can gain an edge on the rest of the market in assessing and dealing with some of these dangers, you have a pathway to success. I am not suggesting that this is easy to do, or that success is guaranteed, but if investment is a game of odds, this can help tilt them in your favor. YouTube Video Datasets Risk Measures, by Industry - Start of 2024 Risk Measures, by Country - Start of 2024 Equity Risk Premiums, by Country - Start of 2024 Cost of Capital, by Industry - Start of 2024 (US & Global) Data Update Posts for 2024 Data Update 1 for 2024: The Data Speaks, but what is it saying? Data Update 2 for 2024: A Stock Comeback - Winning the Expectations Game! Data Update 3 for 2024: Interest Rates in 2023 - A Rule-breaking Year Data Update 4 for 2024: Danger and Opportunity - Bringing Risk into the Equation
In my last post, I looked at equities in 2023, and argued that while they did well during 2023, the bounce back were uneven, with a few big winning companies and sectors, and a significant number of companies not partaking in the recovery. In this post, I look at interest rates, both in the government and corporate markets, and note that while there was little change in levels, especially at the long end of the maturity spectrum, that lack of change called into question conventional market wisdom about interest rates, and in particular, the notions that the Fed sets interest rates and that an inverted yield curve is a surefire predictor of a recession. As we start 2024, the interest rate prognosticators who misread the bond markets so badly in 2023 are back to making their 2024 forecasts, and they show no evidence of having learned any lessons from the last year. Government Bond/Bill Rates in 2023 I will start by looking at government bond rates across the world, with the emphasis on US treasuries, which suffered their worst year in history in 2022, down close to 20% for the year, as interest rates surged. That same phenomenon played out in other currencies, as government bond rates rose in Europe and Asia during the year, ravaging bond markets globally. US Treasuries Investors in US treasuries, especially in the longer maturities, came into 2023, bruised and beaten rising inflation and interest rates. The consensus view at the start of the year was that US treasury rates would continue to rise, with the rationale being that the Federal Reserve was still focused on knocking inflation down, and would raise rates during the yearl. Implicit in this view was the belief that it was the Fed that had created bond market carnage in 2022, and in my post on interest rates at the start of 2023, I took issue with this contention, arguing that it was inflation that was the culprit. 1. A Ride to Nowhere - US Treasury Rates in 2023 It was undoubtedly a relief for bond market investors to see US treasury markets settle down in 2023, though there were bouts of volatility, during the course of the year. The graph below looks at US treasury rates, for maturities ranging from 3 months to 30 years, during the course of 2022 and 2023: Download data As you can see, while treasury rates, across maturities, jumped dramatically in 2022, their behavior diverged in 2023. At the short end of the spectrum, the three-month treasury bill rate rose from 4.42% to 5.40% during the year, but the 2-year rate decreased slightly from 4.41% to 4.23%, the ten-year rate stayed unchanged at 3.88% and the thirty-year rate barely budged, going from 3.76% to 4.03%. The fact that the treasury bond rate was 3.88% at both the start and the end of the year effectively also meant that the return on a ten-year treasury bond during 2023 was just the coupon rate of 3.88% (and no price change). 2. The Fed Effect: Where's the beef? I noted at the start of this post that the stock answer than most analysts and investors, when asked why treasury rates rose or fell during much of the last decade has been "The Fed did it". Not only is that lazy rationalization, but it is just not true, and for many reasons. First, the only rate that the Fed actually controls is the Fed funds rate, and it is true that the Fed has been actively raising that rate in the last two years, as you can see in the graph below: In 2022, the Fed raised the Fed funds rate seven times, with the rate rising from close to zero (lower limit of zero and an upper limit of 0.25%) to 4.25-4.50%, by the end of the year. During 2023, the Fed continued to raise rates, albeit at a slower rate, with four 0.25% raises. Second, the argument that the Fed's Fed Funds rate actions have triggered increases in interest rates in the last two years becomes shaky, when you take a closer look at the data. In the table below, I look at all of the Fed Fund hikes in the last two years, looking at the changes in 3-month, 2-year and 10-year rates leading into the Fed actions. Thus, the Fed raised the Fed Funds rate on June 16, 2022 by 0.75%, to 1.75%, but the 3-month treasury bill rate had already risen by 0.74% in the weeks prior to the Fed hike, to 1.59%. In fact, treasury bill rates consistently rise ahead of the Fed's actions over the two years. This may be my biases talking, but to me, it looks like it is the market that is leading the Fed, rather than the other way around. Third, even if you are a believer that the Fed has a strong influence on rates, that effect is strongest on the shortest term rates and decays as you get to longer maturities. In 2023, for instance, for all of the stories about FOMC meeting snd the Fed raising rates, the two-year treasury declined and the ten-year did not budge. To understand what causes long term interest rates to move, I went back to my interest rate basics, and in particular, the Fisher equation breakdown of a nominal interest rate (like the US ten-year treasury rate) into expected inflation and an expected real interest rate: Nominal Interest Rate = Expected Inflation + Expected real interest rate If you are willing to assume that the expected real interest rate should converge on the growth rate in the real economy in the long term, you can estimate what I call an intrinsic riskfree rate: Intrinsic Riskfree Rate = Expected Inflation + Expected real growth rate in economy In the graph below, I take first shot at estimating this intrinsic riskfree rate, by adding the actual inflation rate each year to the real GDP growth rate in that year, for the US: Download data I will not oversell this graph, since my assumption about real growth equating to real interest rates is up for debate, and I am using actual inflation and growth, rather than expectations. That said, it is remarkable how well the equation does at explaining the movements in the ten-year US treasury bond rate over time. The rise treasury bond rates in the 1970s can be clearly traced to higher inflation, and the low treasury bond rates of the last decade had far more to do with low inflation and growth, than with the Fed. In 2023, the story of the year was that inflation tapered off during the course of the year, setting to rest fears that it would stay at the elevated levels of 2022. That explains why US treasury rates stayed unchanged, even when the Fed raised the Fed Funds rate, though the 3-month rate remains a testimonial to the Fed's power to affect short term rates. 3. Yield Curves and Economic Growth It is undeniable that the slope of the yield curve, in the US, has been correlated with economic growth, with more upward sloping yield curves presaging higher real growth, for much of the last century. In an extension of this empirical reality, an inversion of the yield curve, with short term rates exceed long term rates, has become a sign of an impending recession. In a post a few years ago, I argued that if the slope of the yield curve is a signal, it is one with a great deal of noise (error in prediction). If you are a skeptic about the inverted yield curves as a recession-predictor, that skepticism was strengthened in 2022 and 2023: Download data As you can see, the yield curve has been inverted for all of 2023, in all of its variations (the difference between the ten-year and two-year rates, the difference between the two-year rate and the 3-month rate and the difference between the ten-year rate and the 3-month T.Bill rate). At the same time, not only has a recession not made its presence felt, but the economy showed signs of strengthening towards the end of the year. It is entirely possible that there will be a recession in 2024 or even in 2025, but what good is a signal that is two or three years ahead of what it is signaling? Other Currencies The rise in interest rates that I chronicled for the United States played out in other currencies, as well. While not all governments issue local-currency bonds, and only a subset of these are widely traded, there is information nevertheless in a comparison of these traded government bond rates across time: Note that these are all local-currency ten-year bonds issued by the governments in question, with the German Euro bond rate standing in as the Euro government bond rate. Note also that during 2022 and 2023, the movements in these government bond rates mimic the US treasuries, rising strongly in 2022 and declining or staying stable in 2023. These government bond rates become the basis for estimating risk-free rates in these currencies, essential inputs if you are valuing your company or doing a local-currency project analysis; to value a company in Indian rupees, you need a rupee riskfree rate, and to do a project analysis in Japanese yen, a riskfree rate in yen is necessary. While there are some who use these government bond rates as riskfree rates, it is worth remembering that governments can and sometimes do default, even on local currency bonds, and that these government bond rates contain a spread for default risk. I use the sovereign ratings for countries to estimate and clean up for that default risk, and estimate the riskfree rates in different currencies at the start of 2024: Download data Unlike the start of 2022, when five currencies (including the Euro) had negative riskfree rates, there are only two currencies in that column at the start of 2024; the Japanese yen, a habitual member of the low or negative interest rate club, and the Vietnamese Dong, where the result may be an artifact of an artificially low government bond rate (lightly traded). Understanding that riskfree rates vary across currencies primarily because of difference in inflation expectations is the first step to sanity in dealing with currencies in corporate finance and valuation. Corporate Borrowing As riskfree rates fluctuate, they affect the rates at which private businesses can borrow money. Since no company or business can print money to pay off its debt, there is always default risk, when you lend to a company, and to protect yourself as a lender, it behooves you to charge a default or credit spread to cover that risk: Cost of borrowing for a company = Risk free Rate + Default Spread The question, when faced with estimating the cost of debt or borrowing for a company, is working out what that spread should be for the company in question. Many US companies have their default risk assessed by ratings agencies (Moody's, S&P, Fitch), and this practice is spreading to other markets as well. The bond rating for a company then becomes a proxy for its default risk, and the default spread then becomes the typical spread that investors are charging for bonds with that rating. In the graph below, I look at the path followed by bonds in different ratings classes - AAA, AA, A, BBB, BB, B and CCC & below - in 2022 and 2023: As with US treasuries, the default spread behaved very differently in 2023, as opposed to 2022. In 2022, the spreads rose strongly across ratings classes, and more so for the lowest ratings, over the course of the year. During 2023, default spreads reversed course, declining across the ratings classes, with larger drops again in the lowest ratings classes. One perspective that may help make sense of default spread changes over time is to think of the default spread as the price of risk in the bond market, with changes reflecting the ebbs and flows in fear in the market. In my last data update, I measured the price of risk in the equity market in the form on an implied equity risk premium, and chronicled how it rose sharply in 2022 and dropped in 2023, paralleling the movements in default spreads. The fact that fear and risk premiums in equity and bond markets move in tandem should come as no surprise, and the graph below looks at the equity risk premiums and default spreads on one rating (Baa) between 1928 and 2023: Download data For the most part, equity risk premiums and default spreads move together, but there have been periods where the two have diverged; the late 1990s, where equity risk premiums plummeted while default spreads stayed high, preceding the dot-com crash in 2001, and the the 2003-2007 time periods, where default spreads dropped but equity risk premiums stayed elevated, ahead of the 2008 market crisis. Consequently, it is comforting that the relationship between the equity risk premium and the default spread at the start of 2024 is close to historic norms and that they have moved largely together for the last two years. Looking to 2024 If there are lessons that can be learned from interest rate movements in 2022 and 2023, it is that notwithstanding all of the happy talk of the Fed cutting rates in the year to come, it is inflation that will again determine what will happen to interest rates, especially at the longer maturities, in 2024. If inflation continues its downward path, it is likely that we will see longer-term rates drift downwards, though it would have to be accompanied by significant weakening in the economy for rates to approach levels that we became used to, during the last decade. If inflation persists or rises, interest rates will rise, no matter what the Fed does. YouTube Video Data US Treasury Rates in 2022 and 2023 Intrinsic Riskfree Rates and T.Bond Rates - 1954 to 2023 ERP and Default Spreads: 1960 - 2023 Data Update Posts for 2024 Data Update 1 for 2024: The Data Speaks, but what is it saying? Data Update 2 for 2024: A Stock Comeback - Winning the Expectations Game! Data Update 3 for 2024: Interest Rates in 2023 - A Rule-breaking Year
Heading into 2023, US equities looked like they were heading into a sea of troubles, with inflation out of control and a recession on the horizon. While stocks had their ups and downs during the year, they ended the year strong, and recouped, at least in the aggregate, most of the losses from 2022. That positive result notwithstanding, the recovery was uneven, with a big chunk of the increase in market capitalization coming from seven companies (Facebook, Amazon, Apple, Microsoft, Alphabet, NVidia and Tesla) and wide divergences in performance across stocks, in performance. As we move into 2024, it looks like expectations have been reset, with most forecasters now expecting the economy to glide in for a soft landing and interest rates to decline, and while that may seem like good news, it will represent a challenge for equity market investors. Looking Back Almost a year ago, I wrote a post about what 2023 held for stocks, and it reflected the dark mood in markets, and in the face of investor gloom, looked at how the expectations game would play out for equities. In that post, I noted that if inflation subsided quickly, and the economy stayed out of a recession, stocks had upside, and that is the scenario that played out in 2023. Stocks ended the year well, with November and December both delivering strong up movements, and while this left investors feeling good about the year, it was a rocky year. In the graph below, I look at the monthly levels on the index and price returns, by month: On a month-to-month basis, stocks started the year well and had a good first half, before entering a tough third quarter where they gave back most of those gains. Over the course of the year, the S&P 500 rose from 3840 to 4770, an increase of 24.23% for the year, which when added to the dividend yield of 1.83% translated into a return of 26.06% for the year: To get historical context, I compared the returns in 2023 to annual returns on the S&P 500 going back to 1928: Download historical return data It was a good year, ranking 24th out of the 95 years of data that I have in my dataset, a relief after the -18.04% return in 2022. The solid comeback in stocks, though, came with caveats. The first is that it was an uneven recovery, if you break stocks down be sector, which I have, for both US and global stocks, in the table below: As you can see, technology was the biggest winner of the year, up almost 58% (44%) for US (global) stocks, with communication services and consumer discretionary as the next best performers. Energy, one of the few survivors of the 2022 market sell-off, had a bad year, as did utilities and consumer staples. Breaking equities down by sub-region, and looking across the globe, I computed the change in aggregate market capitalization, by region: While US stocks accounted for about $9.5 trillion of the $14 trillion increase in equity market capitalization across the world, two regions did even better, at least on a percentage basis. The first was Eastern Europe and Russia, coming back from a massive sell-off in the prior two years and the other was India, which saw an increase of $1 trillion in market cap, and a 31.3% increase in market capitalization. Looking forward While there is comfort in looking backwards, slicing and dicing data in the hope of getting clues for the future, investing is about the future. Much as we like to believe that history repeats itself, and find patterns even when they do not exist, the nature of markets makes them difficult to forecast, precisely because they are driven not by what actually happens to the economy, inflation and other fundamentals, but by how these results compare to expectations. Going into 2024, investors are clearly in a better mood about what is to come this year, than they were a year ago, but they are pricing in that better mood. To capture the market's mood, I back out the expected return (and equity risk premium) that investors are pricing in, through an implied equity risk premium: Put simply, the expected return is an internal rate of return derived from the pricing of stocks, and the expected cash flows from holding them, and is akin to a yield to maturity on bonds. To see how expectations and pricing have changed over the course of the year, I compare the implied equity risk premium (ERP) from the start of 2023 with the same number at the start of 2024 2023 ERP & 2024 ERP At the start of 2023, in the midst of the market's pessimism of what the coming years would deliver, stocks were priced to earn a 9.82% annual return and a 5.94% equity risk premium. In contrast, at the start of 2024, the lifting of fear has led to higher prices, a more upbeat forecast of earnings and an expected return of 8.48% and an equity risk premium of 4.60%. I do compute this expected return and the equity risk premium at the start of each month, and the last 24 months have been a roller coaster ride: While equity risk premiums and expected returns rose strongly in 2022, registering the largest single-year increase in history, they declined over 2023, as hope has gained an upper hand over fear. To the question of whether 8.48% is a reasonable expectation for an annual return for US stocks, and 4.60% a sufficient equity risk premium, I looked at the historical estimates for these numbers going back to 1960: Download historical ERP While stocks had expected returns exceeding 10% for much of the 1970s and 1980s, the culprit was high interest rates, and as interest rates have declined in this century, expected returns have come down as well. The post-2008 time period also was a period of historically low interest rates, and expected returns bottomed out in 2021, before rising again in 2022. In the table below, I look at the expected returns and equity risk premiums at the start of 2022, 2023 and 2024 against the distribution of the corresponding variables between 1960 and 2024: Download historical ERP It is comforting, if you are an equity investor, to see that the expected returns are only slightly lower than the median value over the longer period, and the equity risk premium is above historical norms. Needless to say, there are other metrics, measuring the cheapness or expensiveness of equities, that investors may find more troubling. In particular, the earnings yield (the inverse of the PE ratio) for US equities will give investors pause: Download historical EP Note that the EP ratio, after a surge last year, has dropped back towards 2022 levels, with the caveat being that treasury bond rates are much higher now than they were then, an attractive alternative to equities that did not exist two years ago. Taking a Stand I am not a market timer, but I do value the market at regular intervals, more to get a measure of what the market is pricing in, than to forecast future movements. In valuing the index, I follow the intrinsic value rulebook, where the value is determined by expectations of cash flows in the future, discounted back to adjust their risk. To get expected cash flows, I start with expectations of earnings from the equities that comprise the index. For the S&P 500, the most widely followed equity index, I use the consensus estimates of aggregate earnings for 2024 and 2025, from analysts. I know that mistrust of analysts runs high, and the perception that they are cheerleaders for individual companies is often well founded, but I will stick with these forecasts for a simple reason. Having tracked analyst forecasts for four decades,I have found that analyst estimates of aggregated earnings for the index are unbiased, with analysts under estimating earnings in almost as many years as they over estimate them. The cash flows to equity investors, especially in the United States, have increasingly taken the form of buybacks, not just supplementing but supplanting dividends. In 2023, dividends and buybacks on the S&P 500 index amounted to $1.367 trillion, 164.25 in index units, with 57.6% of these cash flows coming from buybacks. As a percent of earnings, the cumulative cash returned represented 74.8% of earnings in that year, representing a decline from payout ratios during this century (2000-2022); the median payout ratio for this period was 83%. With these earnings and cash flows as starting points, and assuming that the treasury bond rate of 3.88% is a fair interest rate, I value the S&P 500: Download valuation spreadsheet Note that I forecast earnings beyond 2025, by assuming that growth scales down to the growth rate of the economy, estimated to be roughly equal to the riskfree rate. Unlike early in 2023, when stocks looked slightly under valued, with consensus earnings numbers and prevailing rates, stocks look over valued by about 9.2%, with a similar structure today. As with any market valuation, there are risks embedded in this value. First, the consensus view that the economy will come in for a soft landing may be wrong, with a recession or a stronger recovery both in the cards; the earnings numbers will be lower than analyst estimates in a recession and higher with a stronger economy. Second, while the market is building in expectations of interest rates declining in 2024, a significant portion of that optimism comes from a delusion that the Fed can raise or lower rates at well. After all, the treasury bond rate, a much stronger driver of equity values than short term treasury rates, remained unchanged in 2023, even as the Fed repeatedly raised the Fed Fund rates, and it is very likely that the future path of the treasury bond rate will depend more on the vagaries of inflation than on the whims of Jerome Powell. In the graph below, I look at the fair index level as a function of assumptions about earnings surprises and interest rates: Note that I report the fair index values currently, and to convert them into target levels for the index a year from now, you have to take the future value of the index, using the expected return on stocks (net of dividend yield). For instance, to get the expected index level at the end of 2024, if rates stay at around 4% and earnings come in 10% above expectations, is as follows: Fair value of the index in current terms = 5202 Expected annual return on equities = T.Bond rate + ERP = 4% + 5% = 9% Expected price appreciation on equities = Expected annual return - Dividend yield = 9% - 1.5% = 7.5% Expected index level on 12/31/2024 (r =4%, Earnings 10% above expected) = 5202 (1.075) = 5592 As you can see, you would need earnings to come in above expectations, for the current index level (4750 on January 16) to be justified, with lower interest rates providing an assist. While what-if tables like the one above are useful tools for dealing with uncertainties, a more complete assessment of uncertainty requires that I be explicit about the uncertainties I face on each input, resulting in a simulation: Simulation run with Crystal Ball, an Excel add-on Not surprisingly, with uncertainties built in, the fair value of the index has a wide range, but using the first and ninth decile, a reasonable range for the fair value would 3670 - 5200, and at the January 16 closing level of 4750, there is about a 70% chance that the market is over valued. I am sure that you will disagree with one or more of the inputs that I have used to value the index, and I welcome that disagreement. Rather than point out to me the error of my ways, please download the spreadsheet containing the intrinsic valuation, and you should be able to replace my assumptions about earnings, cash payout and interest rates, and arrive at your own estimates of index value. Caveat emptor! Before you take my market prognostications at face value, please consider my open disclosure that I am a terrible market timer and try to avoid it in my investing. In short, I do not plan to act on my market valuation by buying puts on the index, or scaling down by portfolio's equity exposure. If you are wondering why I bother valuing the index, there are two reasons. First, there are times in the past, when the overvaluation of the market is so large that it operates as a red flag on investing in equities, as an asset class, in general. That signal worked in early 2000 but did not in early 2008, and it is thus a noisy one. Second, and more generally, though, valuing the market allows you to make sense of, and tolerance for, bullish and bearish views on the market that may diverge from your own views. Thus, investors and analysts who believe that rates will continue to decline, with a strong economy delivering higher-than-expected earnings, will see significant upside in this market, just as investors and analysts who believe that stubbornly higher inflation will cause rates to rise, and that earnings will come in well below expectations will be more likely to be part of the doomsday crowd. Just as in 2023, there will be times in 2024 when one side or the other will think that it has decisively won the argument, just to see a reversal in the next period. YouTube Video Datasets Historical Returns on Stocks, Bonds, Gold and Real Estate - 1928 -2023 Historical Implied Equity Risk Premiums and Expected Returns - 1960- 2023 Spreadsheets Implied ERP calculator - January 1, 2024 Valuation of the S&P 500 on January 1, 2024
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