The Price of Risk: An Equity Risk Premium Monologue!

   I start my valuation classes with a question of whether valuation is an art or a science, and I argue that it is neither; it does not have the precision that characterizes a science and unlike an art, it does come with principles that constrain you on what you can and cannot do. I describe valuation as a craft, where you learn as you value companies, and in the process, there are times where you question how it is practiced, and try to find ways to do it better. I have learned my share of lessons in the four decades that I have practiced valuation, and I have often abandoned standard practices, in the hope of developing better ones. There is no input in valuation where I have found myself questioning existing practices more than in estimating the price of risk in equity markets, i.e., the equity risk premium, and I have wrestled with ways of coming up with alternatives. That endeavor was pushed into high gear by the 2008 market crisis, when I started to pay more attention to how markets price risk, what causes that price of risk to change over time and the limitations in the ways that we estimate that price of risk in financial analysis.

Status Quo and Standard Practice

    Leading into 2008, I had long been skeptical about how we approached the estimation of equity risk premiums,  essential ingredients in hurdle rates in corporate finance and discount rates in valuation. It was (and still remains) standard practice to look at historical data, almost entirely from the US, on what stocks had earned over treasuries, and use that historical equity risk premium as the best estimate of the equity risk premium for the future, That approach would have yielded an equity risk premiums of between 5.5% to 14.5%, at the start of 2026, depending on the time period used, the way we compute averages and what we use as the riskfree rate.

These historical equity risk premiums are not only backward-looking and very noisy (see the standard errors), but they allow bias to easily creep in, through the choice of equity risk premiums, with bullish (bearish) analysts picking lower (higher) numbers.  Disconcertingly, they also move in the wrong direction, falling during crises (as historical returns get updates) and rising during good times.

A Forward-Looking Alternative

    To counter the problems that I saw with historical risk premiums, I started estimating forward-looking equity risk premiums, by essentially backing out from stock prices and expected cash flows, the expected return (internal rate of returns) that markets were pricing into stocks. 

That approach yields forward-looking equity risk premiums, and while there is estimation error in the expected earnings growth and payout numbers, it yields vastly more precise estimates that are also model-agnostic. Using this approach, the equity risk premium at the start of 2026 was 4.23% (over the US treasury bond rate):

Note that this estimation is model-agnostic, and is simply a measure of what markets are pricing in, given expected cash flows at the moment.

ERP Estimation during Crises

    Unlike historical equity risk premiums, these implied premiums are sensitive to market gauges of fear and greed, and change, as those change. In fact, I computed the ERP, by day, during the 2008 market crisis, and you can see the shifts during that 14-week period below:

Note that the crisis started with the equity risk premiums at 4.2% on September 12, 2008m but almost doubled over the next two months, as stocks went into free fall. To me, these implied equity risk premiums made far more intuitive sense, rising as market fears about banks and the economy rose.

    I have continued with the practice of estimating equity risk premiums, by day, during market crises (real or perceived). Here, for instance, is my assessment of the UK market in 2016 in the weeks leading up to the Brexit vote, the market reaction to COVID and the global economic shutdown in 2020, and how the tariffs roiled markets last year. In fact, as we wrestle with an war and oil price induced market shock in March 2026, I started my daily estimates for the ERP on March 1 and will report on how that price has changed over the last two weeks, in the next section.

Equity Risk Premiums - Lessons Learned

        The process of estimating implied equity risk premiums on a continuing basis is driven less by intellectual curiosity and more by my need for these numbers, when I value companies. That process has taught me three lessons about equity risk premiums, and I have responded by altering my practices.

    

1. The equity risk premium is a dynamic and shifting number, and a good estimate of the premium should reflect this volatility. Using an equity risk premium that is different from the implied equity risk premium makes every valuation a joint judgment on what you think about the company and what you think about the market. Put simply, sticking with a 4% equity risk premium during a crisis, when the implied risk premium has surged to 6% will lead you to find most companies to be undervalued, almost entirely because you think that the market is undervalued (not the company). In my view, a company valuation should be market-neutral, and the only way you can get there is by using a current implied equity premium.

My response: Rather than compute the implied equity risk premium at the start of every year, and using that premium over the course of the year, I shifted to computing the equity risk premium for the S&P 500 at the start of every month, in September 2008.  I report those numbers on my entry page to my website (damodaran.com) and use them to value companies during the course of the month. You can find these monthly equity risk premium estimates by going to this link. 

2. The implied equity risk premium is a consolidated metric for market pricing, and every debate or discussion about whether the market is under or over priced can be reframed as a debate about whether the implied equity risk premium is too low (over pricing), just right (fairly priced) or too high (under pricing). Since the implied ERP incorporates the level of interest rates, expected growth and cash payout, it is a more complete assessment of the market than looking at dividend yields and earnings yields (or variants of PE ratios), two widely used proxies for market pricing. In this post, I took an extended look at how these different measures of equity risk premiums measure up, in terms of predicting future equity returns.

My response: I have been open about my discomfort with timing markets, but when I am asked what I think of the overall market (Is it too high? Is it a bubble?), I first measure the current equity risk premium and then assess it against history. I used this technique to assess US equities at the start of this year in a post, with the accompanying graph: 

My conclusion, at the start of 2026, was that while stocks were richly priced using almost every conventional metric (high PE ratios, low dividend yields), the implied equity risk premium was in line with what US stocks have generated over the last 65 years. That said, I did note that 2025 was a tumultuous year, with tariffs making the news and the post-war dollar-centric global economic system starting to fray, and argued that the market seems to be too sanguine about catastrophic risk. Almost on cue, two weeks ago, bombs started falling in the Middle East, and US equities and bonds have been struggling to price in the effects of higher oil prices. In keeping with my practice of estimating equity risk daily, during troubled times, I did compute the implied ERP for the S&P 500 every day, during the last two weeks (Feb 27- March 13):

Oil is up to over a hundred dollars a barrel and the S&P 500 is down, but so far, the market is not behaving as if it is in crisis mode. The equity risk premium, which started March at 4.37% has risen, but only to 4.51%, over the two weeks. In fact, it is the ten-year US treasury bond that has had the bigger surge, up from 3.97% at close of trading, on February 27, to 4.28% at close of trading, on March 13, indicating inflation fears are trumping other market concerns right now. All of this could change next week or the week after, and I will continue to track the equity risk premiums, by day, until the market settles in.

3. The equity risk premium is an essential ingredient into almost every part of financial analysis, incorporated into hurdle rates in corporate finance, discount rates in valuation and in expected returns on equity in financial planning. Given this centrality, I was surprised how little attention it has received from both academics and practitioners, when I looked for references. There is very little usable academic research on equity risk premiums specifically, though there is a great deal on asset pricing and risk. As for practitioners, they have, for the most part, relied on historical risk premiums, and often obtain these premiums from services that summarize the historical data. When I took my first finance class, the historical risk premiums came from data from Ibbotson Associates, that contained annual return data on stocks, bonds and bills. That data was acquired by Duff and Phelps, where it became part of a voluminous book on cost of capital, but much of what that book had to say about equity risk premiums reflected slicing and dicing the historical data, hoping to get further insights, and for the most part failing, because of the noisiness in the data. The US historical data is now in the hands of Kroll, but there is little of value that be extracted by doing deeper and deeper mining expeditions on historical return data. In fact, if you are a fan of historical equity risk premiums (I am not, as you can guess), my suggestion would be to use the Credit Suisse Yearbook, which looks at historical equity risk premiums in 20 markets over more than a hundred years, and does not suffer from the selection bias of focusing on just US data.

My response: I am a practitioner and I decided, for my own understanding, to pull together everything I knew about equity risk premiums into a paper that I wrote in early 2009, and shared online that year. Practitioners seemed to find it useful, and I have updated that paper every year since, at the start of the year. It has grown over time, as I have sought to pull together new findings on equity risk premiums and incorporate changes in markets, and my seventeenth annual update is now ready. I have to confess that at this point, much of the change is data-driven, with tables and graphs updated to include the most recent year's data, but I hope you still find it useful. The paper resides on the social science research network (SSRN), an Elsevier-run platform for working papers in the social sciences. Unlike most of the other papers on that platform, I have no interest is ever publishing this paper, but you are welcome to download not just the paper, but all of the data that goes with the paper. 

Equity Risk Premiums - The 2026 Edition

    If you do get a chance to download the paper, I should warn you ahead of time that it long (153 pages), unexciting and entirely directed at practitioners. It is modular, though, and it is broadly broken down into the following sections:

1. The Determinants of Equity Risk Premiums: Given that equity risk premiums represent the price of risk in the market, it should come as no surprise that almost everything that happens in the market, political or economic, affect its level. The picture below summarizes the determinants, and you can find more details in the paper:

As you can see, all of these variables can and will change over time, explaining why the ERP should be a volatile number.

2. Historical Equity Risk Premiums (and spin offs): I spend a section of the paper discussing historical equity risk premiums, examining the statistical properties that make it a faulty approach, and why a belief in mean reversion has made it the status quo. While most of the historical equity risk premiums that you see reported in practice come from the US and are based upon the Ibbotson data going back to 1926, I also look at historical data that goes back further (to 1871) as well as historical premiums in the rest of the world. The historical data on returns in the US has also been mined by services to extract premiums that have been earned by subsets of stocks, and since these premiums often get used by practitioners, I look at the efficacy of these premiums. I specifically look at the small cap premium, a widely used add on in valuation, and not that not only has it been noisy over the entire time period (1926-2025), but that it has disappeared since 1981:

The fact that the small cap premium endures in practice is a testimonial to how once bad practices become embedded in valuation, they never leave.

3. Equity Risk Premiums, by country: While I do have a companion paper that explores country risk in detail, that I update in the middle of the year, I describe my process for estimating equity risk premiums, by country, starting with a mature market premium, and then adding on additional premiums, based on country default risk spreads (based on ratings and sovereign CDS spreads).

4. Implied Equity Risk Premiums and Alternatives: In this section, I start with a description of an intrinsic value model for the market, and use that model to illustrate what you would need to assume for the dividends yield or earnings yield to become reasonable proxies for the equity risk premiums; for the latter, for instance, you have to assume either that there is no earnings growth or that if there is growth, it is value neutral. I then use the full version of the model, allowing for higher growth and cash payout that includes buybacks, to derive my implied equity risk premium estimates. I also look at how my implied equity risk premium estimates relate to other risk proxies (default spreads on bonds, VIX etc.) and how they change over time, as the riskfree rate changes.

5. Efficacy of ERP Estimates: The test of whether an equity risk premium estimate is a good one is in the data, since equity risk premiums measure expectations of what investors hope to earn on equities in future periods. In the last section of the paper, I examine the predictive efficacy of alternative measures of equity risk premiums, by looking at their correlation with actual stock market returns in the next year, the next five years and the next ten years:

Since a good ERP estimate should have a large positive correlation with actual returns on stocks in future years, the current implied premium does best for the five-year and ten-year return, and the historical risk premium does worst, with actual returns increasing (decreasing) when it decreases (increases). In bad news for market timers, none of the equity risk premium approaches does well at forecasting next year's actual return, and even at the longer time periods, there is significant error in predictions.

Paper

1. Equity Risk Premiums (ERP): Determinants, Implications and Estimates - The 2026 Edition
   

Data

1. Historical returns on US asset classes (1928 -2025)
2. Implied Equity Risk Premiums, at end of year (1960-2025)
3. Implied Equity Risk Premiums, start of each month (Sept 2008 - March 2026)
4. Equity Risk Premiums, by country, at the start of 2026

Spreadsheets

1. Spreadsheet to compute implied ERP - S&P 500 on February 27, 2026

AI Scenarios: From Doomsday Destruction to Do-Nothing Bots!

     When Chat GPT made its debut on November 30, 2022, it unleashed the hype of AI, and in the three years since, AI has taken on an outsized role not just in markets, but also in our lives. For much of the time, the AI story has been told by its advocates and its salespeople, and the companies in the AI ecosystem have benefited. Not surprisingly, given that its narrators benefit from this growth, that story has emphasized the positive, with dazzling AI use cases and optimistic extrapolation of the productivity gains from its adoption. In the last few months, we have seen cracks emerge in the AI story, with investors wondering when, and in what form, the immense investments in AI architecture will pay off, and how if they pay off, the businesses that they disrupt will fare. That disquiet has played out as negative market reactions to new AI investments at Meta and Amazon, a markdown in software company market capitalizations and in a sell off last week, in response, at least partially, to an AI scenario assessment from Citrini Research, a publisher of macro and stock research. Given that I know very little about the technology of AI, and that my macroeconomic knowhow is pedestrian,  my intent in this post is less about promoting my favored AI scenario, and more about providing a framework for you to develop your own.

The Citrini AI Assessment - Report and Responses

    The Citrini AI assessment came out on February 22, 2026, and it starts with a preface stating that it is presenting a scenario, not a prediction. I do have issues with that opening, but I will come to them later, but the report itself laid out a story for AI that unfolds with a dark end game for the economy, where by June 30, 2028, the AI disruption has unsettled businesses and displaced workers, with unemployment rates rising above 10% and the market down almost 40% in response. There have been other AI doomsayers, but many of those doomsday scenarios are built around the storyline that AI will not live up to its promise, and the pain comes from having over invested trillions of dollars in building its architecture. In contrast, the Citrini AI  story is built on the expectation that not only does AI work well at doing tasks currently performed by white collar professionals, across a range of firms, but its adoption happens very quickly. The pain in the Citrini story comes from that disruption creating substantial job losses, and especially so among higher-earning workers, and the resulting loss of income driving these job losers to cut back on consumption. The ripple effects play out across businesses, with default risks and spreads rising, private credit collapsing and the market and economy pricing in the pain.

    I do think that there are major flaws in the steps leading to the economic implosion in the Citrini assessment, but credit should be given where it is due. I have always been troubled by how much we have worshiped at the altar of disruption in this century, putting the founders of disruptors on pedestals and preaching disruption's virtue. In keeping with Joseph Schumpeter's description of capitalism as built around creative destruction, I do believe that a vibrant and dynamic economy needs a shake-up and challenging of the status quo, but disruption comes with costs to the businesses that are disrupted, and to the people who work in them. There is much to celebrate, as consumers, in terms of choice and price from the growth of online retail, but that does not take away from the devastation that has been wreaked on brick-and-mortar retail and its constituent parts. Ride sharing has brought car service from its nineteenth century ways into the twenty first century, but at the expense of yellow cabs and conventional car service businesses. The reason that many AI advocates took issue with the Citrini report was precisely because it bought into their sales pitch of how AI bots can not only do what lawyers, bankers, software engineers and consultants do, but also do them better, and then asked the question of "what then?.

   The Citrini AI scenario must have hit some targets, because in the days since, we have been flooded with scenarios countering Citrini and arriving at different outcomes. While I was not surprised to see Goldman Sachs, Moody's and JP Morgan jump in with their AI scenarios, with more benign outcomes for the economy, where the job loss and income effects from AI are modest and temporary, I was surprised to see Citadel wade into the argument, with a direct rebuttal to Citrini, which sees a much more positive end game from AI disruption, and is built around three pillars. The first is the current data on jobs and layoffs in the businesses most directly targeted by AI, such as software, where they note that while jobs have been shed, the job losses have been modest, and AI adoption trends don’t see breakouts consistent with the speedy disruption predicted by Citrini. The second is history, where they look at disruptions in the past (PCs, the internet) and note that none of them have been speedy or have created the job losses or economic collapses predicted in the doomsday scenario. The third is grounded in macroeconomics, where they point to the inconsistency of assuming  that a large positive productive shock, from AI’s success, will play out out as large negative shock to the economy and market in which it happens. 

Completing the AI story

    The problem with all of these AI scenarios is that they are rooted in the weakest of responses to uncertainty, which is to either pick a scenario and to describe it in detail, without establishing, at least in qualitative terms, how likely that scenario is, in the first place, or to list out a whole host of scenarios, without making judgments on likelihood on eany of them. It is entirely possible that what Citrini was presenting was a "worst-case" scenario (I read through the report and could not get a sense of if this was so, and the subsequent responses from Citrini have only muddied the waters), a "low likelihood" scenario or the "likely scenario" of how AI will unfold. If it is a likely scenario, and you buy into the pitch, the investment and personal consequences will be dramatic, since it is entirely possible that, if you are a white-collar worker, you may have lost your job by June 2028, and your savings, if invested in stocks, would have taken a beating. If it is a "low likelihood" scenario, and you are exposed, because of your job, age and portfolio composition, you should consider buying protection, but if it is a worst-case scenario, it is almost entirely useless, except for shock value.

Point Estimates and Probabilities

    For much of its history, financial analysis has been built around point estimates, where you identify key drivers, estimate the effects on your bottom line (earnings, cash flows) and make your best judgments. Thus, when valuing a company, you estimate the earnings growth on base year earning, how much you will reinvest of those earnings to grow to get to cash flows, and discount those cash flows back at a risk-adjusted rate to get to value. The problem with point estimates, where almost everything is uncertain is that you will be wrong 100% of the time, though you may still make money, if you are wrong in the right direction.

    Financial analysts and economics have been slow in adopting and using probabilistic approaches, where point estimates are replaced by distributions, and a single judgment on outcome by a distribution of outcomes. One reason, at least early on, was that economists and financial analysts often did not have rich enough data or powerful enough tools to use decision trees, simulations or scenario analysis in making their macroeconomic and investment judgments, but that is no longer true. Another reason may be that many in this group are uncomfortable with statistical distributions or probability estimates and stay away from using them, because of that discomfort. The third reason, at least for a subset of analysts, is a concern that being open about estimates and the errors in those estimates, which is visible to all in probabilistic approaches, will be viewed as a sign of weakness or lack of conviction on their part. I have a short paper on using probabilistic approaches, where I look not only at when you may want to use which approach (I look at decision trees, simulations and scenario analysis) but also have a short review of statistical distributions, if you are interested. 

    Since Citrini specifically titled their AI thought piece as a scenario, I will stick with scenario analysis in this post. In its most sloppy form, and one that has been around for decades, scenario analysis has taken the form of best case - base case - worst case scenarios, an almost useless exercise, since there are almost no risky investments that are going to pass muster under the worst case scenario, no matter how good they are, or are going to fail under the best case scenario, no matter how bad they are. A scenario analysis, done right, should look at scenarios that cover all possible outcomes on an investment or decision, and for completion, need probabilities attached to these scenarios, which can then be used by a decision maker to estimate expected values. That will be almost impossible to do if you are trying to work out future pathways to AI, since it is so early in the process and so little is known about outcomes. 

    There is an alternate path for scenario analysis that is less information-intensive and thus more feasible, and it draws on the 3P test that I  use when valuing companies, where my company valuation narrative has to start with the possible test (it can happen) to being plausible (which requires more backing) and then on to the probable (where you can estimate a likelihood). In the context of scenario analysis, this would require that you categorize scenarios into their the three groupings:

The discussion around where AI is going would become much healthier if scenario proponents were required to state where their proposed scenarios fall in this spectrum. Citrini, for instance, could have saved itself from some of the backlash, if the writer of the AI doomsday report had specified that it was a possible, but not quite plausible scenario.

The AI Disruption - Gaming the Outcomes

   In the last week, I have seen at least a dozen scenarios touted by individuals and entities, many of whom I respect, and I must confess that I am whipsawed. If, like me, you are drowning in these scenarios, with very different results and outcomes, the only way to retain your sanity and to take ownership of this process is for you to develop a framework where you can not only put each of these scenarios to the 3P test, but also to develop your own assessment of how AI will play out for businesses, investors and the economy.

1. The Disruption - Form and Speed

    The first set of questions that you need to address in the AI story relate to how the AI disruption will evolve, both in form and timing, and to then trace out the aftereffects. 

1. AI Disruption Magnitude - Worker Displacement versus Productivity-enhancing Tools:  If you listen to some of AI’s lead players, AI will have the capacity to replace workers across multiple businesses, as it develops strengths that go beyond the purely mechanical. One reason that the AI effect on unemployment is so large in the Citrini doomsday scenario is because AI’s reach in the scenario is not just restricted to replacing programmers in software but extends to replacing white collar workers in other technology businesses, financial intermediaries, banking and consulting. In contrast, Citadel’s more benign AI reading comes from AI displacing workers in a smaller subset of businesses, while providing tools in others. At the other end of the spectrum, there are still some who believe that when all is said and done, AI will provide tools to workers that may save them time, but will not be powerful or dependable enough to replace them.
2. AI Disruption Speed: Here again, there is disagreement, with some AI optimists believing that its disruption of regular businesses is imminent, whether displacing workers or in giving them tools. Others believe that AI adoption will take time, partly because the tools need work and partly because businesses and workers are slow to adapt to change. The Federal Reserve in St. Louis has created a tracker of AI adoption rates across users, and while it does not capture the depth of the AI adoption, it does provide a measure of how much familiarity and comfort that users are acquiring, with AI tools. 

With the caveats about survey data in place, there are interesting trends in these surveys. First, the use of Gen AI tools in non-work settings has grown more than its usage at work, an indication perhaps of how personal devices (phones, in particular) have changed technology adoption rates. Second, the time that AI has saved people, at least so far, has been modest, ranging from less than 1% in the accommodation and food businesses to about 4% in information and management of companies. Overall, this graph suggests that AI usage is neither as explosively fast growing nor as much of a time-saver, as its proponents suggest that it is. The pushback, though, is that these are surveys of the general population, and that there are data points indicating that the disruption effects are more substantial including the substantial write down in market capitalizations of software companies and layoffs at tech companies. The announcement by Block, the fintech company founded by Jack Dorsey, that it would it be letting go of almost 40% of its workforce, for instance, and blaming AI's rise for the action, was viewed as an indicator of AI's disruption potential. That is a noisy signal, though, since many tech companies have bloated work forces, and AI gives them easy cover, when correcting past mistakes. 

It is true that there is no crystal ball that you can use to gauge the magnitude and speed of AI disruption, but every AI scenario that you see starts with a judgment on one or both. 

2. The Disruption Aftershocks

    Disruptions create aftershocks, some positive and some negative, and while we often avert our gaze and attention from the latter, a full assessment requires considering both. With AI, the positive effects take the form of higher productivity, as it either allows people to do their jobs more efficiently (with AI tools) or actually replaces people and does their jobs instead, in effect allowing for more output with less labor. Relating back to the different pathways that AI disruption can take, both in form and in form and speed, I would hypothesize that these disruption benefits will be a function of how AI disruption plays out.

Proposition 1: The disruption benefits from AI disruption will be greater from people displacement than from AI productivity tools

Proposition 2: The productivity effects from AI disruption will decrease, at least in economic value terms, the longer it takes for the AI disruption to unfold.

The negative effects of AI, in economic terms, will come from the immediate displacement of people, if AI replaces labor, or from the decrease in employees needed to get tasks done, if AI tools make existing employees more efficient. Here again, I would hypothesize that these disruption costs will be  function of how the disruption plays out.

Proposition 3: The disruption costs from AI disruption will be greater from people displacement than from tools, as those laid off lose income and spending power. 

Proposition 4: The productivity costs from AI disruption will decrease, at least in economic value terms, the longer it takes for the AI disruption to unfold, since time will allow new entrants into labor markets to adjust to a disrupted business world.

Intuitively, the longer it takes AI to find roots in business, the more time it gives workers time to adjust, retrain or move on. As you can see, the scenarios where AI displaces existing employees and happens quickly are the ones with the biggest benefits and the biggest costs, and the scenarios where AI supplies tools to existing employees and happens slowly has the least benefits and costs. Building on this theme, I see the net effect of AI disruption playing out as follows:

If AI disruption displaces existing workforces, across many businesses, and happens quickly, the net effect is likely to be negative, at least in the near term, since the economy will not only have to absorb major layoffs quickly, but also because those laid off will be higher-earning white collar workers. While that maps on to the Citrini doomsday scenario, there is still much to debate about which industries will see the most job displacement and how quickly these workers will find other jobs. There is also a discussion that should follow, even in this negative net-benefit scenario, of how quickly the economy (and workers) will adapt, and if and whether net benefits will turn positive in the long term. If AI job displacement is on a limited scale, and/or takes time to unfold, both the benefits and the costs of the AI disruption become smaller, but the net benefit is more likely to be positive, in the short and long term. Finally, the AI disruption takes the form of tools that make workers more efficient, but not efficient enough to reduce workforces, both the benefits and costs of AI become much smaller. In fact, if these tools take a long time to craft and displace little or no labor you get the AI disruption fizzle, with very small benefits and costs.

3. The 3P Test

    Staying true to my earlier assertion that scenarios without probability estimates are not useful, I will try to put the various AI scenarios that I mapped out in the last section on the  3P continuum.

Let me start with the two possible, but not quite plausible scenarios. The first is the a speedy, massive AI disruption, where AI displaces worker across most businesses, and does so quickly, as visualized by Citrini. It can happen, but given the history of disruption, the limits of AI technology and inertia in the process, it is implausible. At the other extreme, it is possible that AI provides tools to workers that improve productivity marginally, with many ending up being more distractions than tools for productivity, effectively emptying its destructive potential, but that too strikes me as implausible, given what we are seeing in terms of AI capabilities. The most plausible scenarios are ones where AI displaces workers in some industries, such as software and some financial intermediaries, and provides tools that help workers to varying degrees in other businesses. As for probable, I think that disruption will reduce workforces in a subset of businesses, that its tools will include some game changers and that it will take longer to unfold, at least when it comes to monetization, than its advocates think. 

    My justification for why AI disruption will take time is based on a mix of factors. The first is that my (limited) knowledge and experience with AI products is that while they sometimes work magically well and quickly, they do have kinks, coming partly from being unable to separate good data from bad, and partly from their imperfect attempt to be imitate humans. The second is history, where no disruption has ever unfolded without delays and drawbacks; remember that the dot com disruption almost lost its moorings during the market bust in 2001. The third is human nature, where much as employees and managers claim to want to move on to new and better options, they remain attached to old technology and products; typewriters and mimeographs took a while to disappear after PCs stormed the workplace and flip phones persisted well into the smartphone era. 

    There are two reasons why I do think that AI disruption is still going to be significant, in the long term. The first is that some of those making the argument that AI will not displace jobs in the long term are assuming that AI in it more advanced form will look like ChatGPT on steroids or be primarily mechanical in its applications. Even my limited exposure to AI's advanced tools suggests that they have far greater capabilities, and their capacity to mimic human intuition and thought processes is unsettling. The second is the blanket assumption that workers in most white collar jobs will not be easily replaced because they bring training, brainpower and experience into those jobs that will be difficult to replicate. Many white collar workers are bright people with specialized knowledge, but the businesses that hire them put them in straight jackets, pushing mechanics over intuition and rule-driven thinking over principle-driven assessments. In short, it is the nature of the jobs that we have created in many white collar settings  that makes them vulnerable to disruption, not the intelligence or training of the people holding those jobs.

    It is worth noting that in my probable scenario, AI will unfold at different rates in different businesses, and if I were pushed to distinguish between the businesses that will be targeted most (and soonest) from the businesses where it will take more time, and have less impact, I would look at four factors:

There may be some confirmation and hindsight bias in this table, but there is a good reason why software, a relatively young industry, with young companies and employees, has been one of the first targets for AI disruption. it is profitable, its products and services are logical and rule-based and much of it has no regulatory or system protection. Within software, though, i would expect software that requires more user interface to be more resilient to AI disruption than software that operates in the background. This table, though, can help determine which white collar jobs will be most exposed to AI disruption, and which least, and perhaps also explain why blanket statements about job displacement in banking, consulting and law are overwrought. With banking and law, a substantial portion of the work done is to meet legal or regulatory requirements, not fill operating needs. I have written about the inanity and uselessness of fairness opinions in M&A, where bankers opine on whether an acquiring company is paying a "fair" value for a target, but this practice persists because these fairness opinions provide cover against lawsuits that ensue when deals fall apart. My guess is that the Delaware courts are not quite ready for an AI fairness opinion bot to take the stand and defend a deal, even if the quality of its work is better than a human banker. With consulting, where cookbook solutions are more the norm than the exception, it is worth remembering the clients pay consulting fees not for the advice, but so that they have someone else to blame, when things go wrong, and there too, an AI bot will not have the same outsourcing power as an army of bankers with Harvard MBAs from McKinsey.

4. Cui Bono?

    Most of the AI scenarios yield net benefits, and even in the most damaging scenarios, where the AI disruption benefits are overwhelmed by its costs, at least in the short term, you could argue for net positive benefits in the long term. That is good news, but it should taken with a grain of salt, since the distribution of these net benefits across businesses and society will be unequal, and it is possible that the net benefits accrue to a few businesses (and individuals), leaving the rest (businesses and individuals) with net costs.

• The interests of the AI companies and the rest the economy/market will diverge on AI disruption, with the former benefiting if the disruption is across many businesses and happens quickly, and the latter benefiting from a slower disruption restricted to a few businesses. This will be the case even if AI tools add to productivity, since the lower costs that companies acquiring these tools will have as a consequence, may not translate into higher profits, especially if their competitors can pay and acquire the same tools.
• The last few major disruptions, starting with the internet, moving on the China and then the smartphone, have all tilted the playing field in many businesses towards larger companies, making businesses more winner-take-all. It is likely that the AI disruption will play out in similar ways, with the winners winning big, and lots of companies losing out. 
• At the individual level, it is not just plausible, but also likely, that a strong AI disruption will make wealth and income inequality worse, with founders of AI businesses joining the ranks of the  deca-billionaires and centi-billionaires.

There is one final cost that may not be explicit in economic terms, at least immediately, but one that has to enter the discussions, As AI threatens to displace workers in white collar businesses, it is worth remembering that a job is not just an income-generator, but also a source of self esteem and worth. When software engineers, who pride themselves on their coding skills, bankers, who have spent decades becoming excel ninjas, and consultants, who have found inventive ways of packaging cookbook solutions and presenting them as new and inventive, find that AI can do what they have spent a lifetime perfecting almost effortlessly, the psychic damage will be significant. The fact that blue collar workers lost their jobs to the internet and China disruptions faced a similar predicament and were largely ignored also means that there may be more than a hint of schadenfreude in society's response to white collar job losses.

The AI Personal Threat

     If you are looking at these side costs and threat to jobs that will come from the AI disruption, and wondering whether we should opt out, by regulating or restricting its reach, I am afraid that the choice is out of our hands. The genie is out of the bottle, and the only pathway that you have, if you operate in a space where AI is ubiquitous, is prepare for a reality where AI tools can automate and do much of what you do on a daily basis, but where you have to create a niche or moat that still makes you necessary.

    Just about two years ago, I wrote about an AI entity called the Damodaran Bot, that was being developed by Vasant Dhar, my colleague at NYU, and noted that having made all that material that I have developed in my lifetime (classes, books, writing, models, videos) publicly available, I was completely exposed to AI disruption. I have watched that bot develop, with quirks and occasional hiccups, to a point where it can replicate much of what I do almost effortlessly. At the time, though, I did write about what I could do to keep the moat at bay, including the following:

• Generalist vs Specialists: I am a dabbler, an expert in nothing and interested in lots of different things, and I do think that gives me an advantage over a bot that is trained to focus on a topic and drill down. The specialist advantages stem from mastering the vast content in a discipline, but those advantages are diluted with AI entities that can also see that content, but the generalist advantage of using multi-disciplinary thinking with be more difficult for AI to replicate.
• Left and Right Brain: I value companies, and early in my valuation life, I decided that financial modeling was not the right path to value businesses, and that good valuations bridge stories and numbers. If the legend of the right and left brains holds, where the left brain controls logic and numbers and the right brain drives your imagination, a bot will have a tougher time replicating what you do, if you use both sides. That said, I have seen the Damodaran Bot get much better at story telling in the two years that I have watched it, and I need to up my game.
• Reasoning muscle: When faced with questions in the days before the internet, you often had no choice but to reason your way to answers. That may have been time consuming, and your answers might even have been wrong, but each time you did this, you strengthened your reasoning muscles. As we move into a period, where the answer to every question is  online, on Google Search and ChatGPT, we are losing the need to exercise those reasoning muscles, and exposing ourselves to being outsourced by our bots. 
• An idle mind: I am not a voracious reader nor a listener to podcasts, and since I don't have much real work to occupy me, I also have plenty of vacant time, with nothing to do. I use that time to daydream and ponder about questions that capture my imagination, including why someone would pay billions of dollars for a sports franchise (like the Washington Commanders), how to deal with the risk of lava from a volcano hitting a spa and ruining its valuation and how streaming has broken the entertainment business. None of these posts include deep insights, but my guess is that the Damodaran bot would have trouble keeping up with my wandering mind.

With the admission that is may not be enough, and that my bot may soon be able write my books and posts, teach my classes and analyze/present data better than I can, I think that you should all be acting as if a bot with your name is looking over your shoulder and trying to learn what you do, and think about what you can do to keep that bot at bay. 

    There is always the possibility that you are arming yourself for a disruption that fizzles, but I will draw on Pascal's wager to explain why you should prepare for an AI imitator or bot, even if you don't believe that it is imminent:

Pascal, a French mathematician, used the wager to explain why be believed in God, even if he was  doubtful of a heavenly presence, because the expected value from believing in God exceeded the expected cost from not believing. In the context of AI, acting as if an AI presence and competitor is present will make you better at whatever you do, as a teacher, banker, consultant or software engineer, and that will persist, no matter what AI's impact is ultimately. Good luck!

YouTube Video

Data Links

1. Federal Reserve in St. Louis AI Adoption Tracker

AI Scenarios

1. The Citrini AI Doomsday 
2. The Citadel Response to Citrini
3. Dario Amodei on the Upside (and Dangers) of AI
4. Daron Acemoglu on the Economics (and Adoption) of AI
5. It's all going to be good - the Jeremy Siegel view on AI
6. The State of AI: Deloitte, McKinsey

Data Update 8 for 2026: Time for Harvesting - Dividends and Buybacks

    In the data update posts this year, I have wended my way from the macro (equities collectives, the bond market and other asset classes) to the micro, starting with hurdle rates and returns in posts five and six and the debt/equity choice in my seventh post. In this post, I will look at the decision by businesses on how much cash to return to their owners, and in what form (dividends or buybacks), and how that decision played out globally in 2025. I will argue that dividend policy, more than any other aspect of corporate finance, is dysfunctional both for the firms that choose to return the cash and the investors who receive that cash. It is also telling that there are many who seem to view the very act of returning cash as a sign of failure on the part of firms that do so, even though it is the end game for every successful business.

The Dividend Decision

    I start my corporate finance classes with a description of three core decisions that every firm has to make in the course of business, starting with the investment decision, where you try to invest in projects and investments that earn more than your hurdle rate, moving on to the financing decision, where you decide on the mix of debt and equity to use in funding those investments, and ending with the dividend decision, where firms decide how much cash to return to their owners. In the case of privately owned businesses, this cash can be withdrawn by the owners from the business, but in publicly listed companies, it takes the form of dividends or buybacks. In keeping with the notion that these are the cashflows to equity investors, and that those cash flows should represent what is left after (residual) after all other needs have been met, dividends should reflect that status and, at least in principle, be set after investing and financing decisions have been made:

That utopian view of residual cash being returned to shareholders is put to the test by two real-world realities that often govern corporate dividend policy:

1. Inertia: In many companies, dividend policy is set on auto pilot, with dividends this year set equal to dividends in the last year. It is for that reason that the word I would use to describe dividend policy, at least when it comes to conventional dividends, is 'sticky', and you can can see that stickiness at play at US companies, if you track the percentage of companies that increase dividends, decrease dividends or leave them unchanged every year.
   
   
   
   In every single year, from 1988 to 2025, the percentage of companies that pay the same dividends that they did in the previous year outnumbers companies that change dividends, and when dividends are changed, they are more likely to be increased than decreased.
2. Me-tooism: In most companies, managers look to peer group dividend policy for guidance on how much, if any, to pay in dividends. Thus, if you are a bank or a utility, it is likely that you will pay high dividends, because everyone else in the sector does so, whereas technology companies will pay no or low dividends, because that is industry practice. While there are good reasons why some industry groups pay more dividends than others, including more predictable earnings and lower growth (and investment needs), hewing to the peer group implies that there will be outliers in each group (fast-growing banks or a mature technology companies) that will be trapped into dividend policies that don't suit them.

When maintaining or increasing dividends become the end game for a business, you unleash dividend monsters, where investing and financing decisions are skewed to meet dividend needs. Thus, a firm may turn away good investments or borrow much more than it should because it feels the need to sustain dividends. 

I have long argued that dividends, in their sticky form, are unsuitable as cash returns to shareholders, but for much of the last century, they remained the primary or often only way to return cash to shareholders. While buying back stock has always been an option available to US companies, its use as a systematic way of returning cash picked up in the 1980s, and in the years since, stock buybacks have become the dominant approach to returning cash for US companies:

As you can see, in the last decade, more than 60% of cash returned to shareholders took the form of buybacks. The primary reason, in my view, is that buybacks, unlike dividends, are flexible, with companies often reversing buybacks, if macro circumstances change, as was the case in 2008 and 2020. There are other reasons that have been offered for the explosive growth in buybacks, but none of them are as significant. There are some who have argued it is stock-based compensation for managers that is pushing them away from dividends to stock buybacks, but that rationale makes more sense for stock options, where stock prices mater, than for restricted stock. In fact, even as more companies shift to restricted stock as their stock compensation mechanism, buybacks have continued to climb, and they are just as high at companies that have no or very low stock based compensation as at companies with high stock-based compensation. Investor taxes are alway in the mix, since investors are often taxed at different rates on dividends and capital gains, but changes in tax law in the last two decades have reduced, if not eliminated, the tax disadvantages associated with dividends, cutting against this argument. 

    I know that there are many investors, especially in the value investing camp, and quite a few economists, who believe that the shift away from dividends to buybacks is unhealthy, albeit for different reasons. I will return to many of the myths that revolve around buybacks later in this post.

A Rational Cash Return Policy

    If you were designing a sensible cash return policy, it has to start with an assessment of how much cash there is available for a firm to return. Since that "potential dividend" should be the cash left over after taxes are paid, reinvestment has been made and debt repaid, it can be computed fairly simply from the statement of cash flows, as free cashflow to equity:

Note that free cash flow to equity starts with equity earnings, converts those earnings to cash flows by adding back depreciation and other non-cash charges, and then netting out capital expenditures and changes in working capital, with increases (decreases) in working capital reducing (increasing) cash flows. It is completed by incorporating the cash flows from debt, with debt issuances representing cash inflows to equity investors and debt repayments becoming cash outflows. Can free cash flows to equity be negative? Absolutely, and it can happen either because you are a money-losing company, too deep in the hole to dig yourself out, or even a money-making companies, with large reinvestment needs? Obviously, paying out dividends or buying back stock when your free cash flows to equity is violating the simple rule that if you are in a hole, you need to stop digging.

    If your free cash flow to equity is positive, you can choose to return it to shareholders, either in the form of dividends or buybacks, but you are not obligated to do so. In fact, if you have positive free cashflows to equity and you choose to return none or only a portion of that cash flow, the difference accumulates into a cash balance. If you choose to return more than your free cashflow to equity, you will either have to deplete an existing cash balance, or if you run out of cash, go out and raise fresh capital.

A company that systematically holds back on cash that it could have returned will, over time, accumulate a large cash balance, but that, by itself, may not trigger a shareholder response, if shareholders trust the company's managers with their cash. After all, cash invested in liquid and riskless investments, like treasury bills and commercial paper, is a neutral (zero NPV) investment, and leaves shareholders unaffected. If you don't trust management to be disciplined, though, you may punish a company for holding too much cash, effectively apply a "lack-of-trust" discount to the cash. The picture below provides a framework for thinking through the cash return decision, and how it will play out in markets.

As you look at the interplay between earnings, investment needs and potential dividends, you can already see why you should expect cash return policies to change over a company's life cycle:

The cash returns you see in this graph should largely map on to common sense, with start-ups and very young companies, often money-losing and requiring substantial reinvestment to grow, having negative free cash flow to equity (thus requiring equity infusions). Young growth companies  are usually self-funding because internal cash flows may rise to cover reinvestment, but these cash flows are not enough to pay dividends. Mature growth companies have enough cash to return, but stick with buybacks, because they value flexibility. Mature stable companies represent the sweet spot for dividend paying, since they have little in reinvestment needs and large predictable earnings and cash flows. As with everything else in the aging process, companies that refuse to act their age, i.e., young companies that choose to pay dividends or buy back stock or mature companies that insist on holding on to cash, damage themselves and their shareholders.

Dividends in 2025

   I will start the assessment of how much companies returned to shareholders in 2025 by looking at conventional dividends paid by companies, using two metrics. The first metric is the dividend payout ratio, where I divide dividends paid by net income, but only if net income is positive; if net income is negative, and dividends get paid, the payout ratio is not meaningful:

As you can see the median payout ratio is about 35% (59%) for US (global) companies, but in both samples, most companies do not pay dividends. There is a sizable subset of companies (12% of US and 14% of global companies) that pay out more than 100% of earnings as dividends, with multiple reasons for that oversized number including a bad earnings year, a desire to increase financial leverage and partial liquidation plans all coming into play.

    The second metric is the dividend yield, computed by dividing dividends paid by market capitalization, or dividends per share by the market price per share. In the graph below, I look at the distribution of dividend yields across companies in the graph below, in 2025:

Again looking at only dividend paying firms in the US and global samples, the median dividend yield was 1.10% for the former and 2.43% for the latter, with major divergences across sub-regions; note that the percent of dividend paying firms  in the United States has dropped below 30% and even globally, less than half of firms pay dividends. The dividend yield ties into the cost of equity discussion that I initiated in my fifth data update, where I described the cost of equity as the rate of return that investors expect to make on their equity investments. In the United States, for instance, that expected return was about 8.50% at the start of 2026, which would indicate that if you are an equity investor, it is price appreciation that you are dependent on, for the bulk of your equity return.

    The dividend yield for equities has declined over time, with the drop off being most noticeable in the United States. The graph below looks at the dividend yield on the S&P 500 from 1960 to 2025, and how that number has become a smaller and smaller portion of the overall expected return on stocks (which I compute with the implied equity return approach):

In 1960, about half of your expected return on stocks came from dividends and that statistic has trended downwards for the last few decades, and in 2025, it represented less than 15% of the total return on stocks. 

    As a final part of this analysis, I looked at dividend yields and payout ratios, broken down by sector, for both US and global companies:

As you can see, the sectors with the highest percentage of firms paying dividends are financials, real estate and utilities, for both US and global companies, and consumer product companies join in that group, for global companies. In terms of payout ratios, the same three sectors dominate, with energy and real estate returning more than 200% of net income as dividends, in 2025, and posting dividend yields in excess of 6%. Technology companies and communication services have the lowest percent of dividend paying companies and the lowest dividend yields and payout ratios.

    The drop in dividend yields over time for the market, the decline in dividend paying firms and the concentration of dividend paying firms in some sectors has put old time value investing to the test. Ben Graham's strategy of principal protection was built around buying large dividend paying firms and holding on for the long term and it has hit a wall. Any investing strategy built around dividends will result in a portfolio composed of mature and declining firms, and even if you accept that reality, those firms are increasingly concentrated in real estate, banking and utilities. 

Buybacks - Myths and Realities

    As buybacks have soared in the United States, misconceptions and myths about buybacks have also surged, with some myths used to back up the argument that buybacks are unhealthy and should therefore be banned and others presented as the basis for buybacks as good, representing cannot-lose strategies to beat  the market. I will start with the myths that are used to argue against buybacks first, before moving on to those that are used to justify it:

1. Myths in favor of the argument that buybacks are bad and should be restricted or stopped

Myth 1.1: Buybacks are a US phenomenon

Reality 1.1: Buybacks are becoming a global phenomenon

    When US firms first started buying back stock in the 1980s, it is true that is was almost entirely or primarily a phenomenon restricted to the US, with large parts of the world restricting or banning the use of buybacks to prevent price manipulation by companies. That is no longer the case, and companies around the world have taken to buybacks, as a flexible alternative to dividends, have adopted the practice. In 2025, I looked at dividends and buybacks from companies around the world:

Companies in the United States are still in the lead in the buyback race, buying back $1.153 trillion in stock in 2025, close to 60% of overall cash returned. Canada, the UK, and Japan are not far behind with more than 35% of cash returned taking the form of buybacks, and the EU and environs, often the slowest to adapt to change, saw almost 29% of cash returned in buybacks. For a variety of reasons, including poor corporate governance and regulatory restrictions, Africa & the Middle East, Eastern Europe and much of south and southeast Asia return relatively little in buybacks.

Myth 1.2: Buybacks are wasteful and reduce corporate investment

Reality 1.2: Buybacks redirect corporate investment from mature companies to growth businesses

    The argument that buybacks are wasteful often come from using a firm as a self-contained economic unit, and noting that money used on buybacks cannot be reinvested back into the firm. That is absolutely true, but the cash that goes into buybacks goes to investors and mostly goes back into the market, as investments in other companies. While there are clearly exceptions, where companies that should be investing back into their businesses use that cash to buyback stock, the companies that are the biggest buyers of their own stock are mature firms with insufficient investment opportunities and the companies that have the cash redirected into them need that cash to fund their growth. You can see this play out, when you look at stock buybacks broken down, by age decile (based upon corporate age) for US and global companies:

As you can see, younger companies are not only less likely to buy back stock, but also return less cash in dividends and buybacks, at least as a percent of market capitalization than older companies. Using the life cycle perspective, this suggests that cash is rotating out of older, more mature businesses into younger businesses. I would argue that the difference between geographies where buybacks are rare and geographies where buybacks are common is not in how much corporate investment there is, but in where that investment is directed, with the former investing investing back into declining businesses and the latter funding higher growth and newer businesses.

Myth 1.3: Buybacks are funded with debt are are making companies too highly levered

Reality 1.3: Buybacks are primarily funded with free cash flows to equity and even as buybacks have surged, debt ratios have decreased.

    I am not a great believer in case studies precisely because anecdotal evidence is spun into backing priors and preconception.s There are, of course, firms that have dug themselves into a hole by buying back immense amounts of stock, and funding those buybacks with debt, but the aggregate debt ratios for US non-financial service firms, with debt to capital ratios measured against both book and market, have declined over the last four decades, even as buybacks have surged. 

If your response is that not all companies buy back stock, and that debt ratios has risen at companies that buy back stock, a comparison of debt ratios (debt to EBITDA and debt to capital) for US firms that bought back stock in 2025 versus those that do not dispels that argument:

If firms are borrowing money to fund buybacks, it is clearly not showing up in the statistics, since companies that bought back stock had much lower debt loads than the companies that did not, a simplistic comparison, but one that carries heft.

Myth 1.4: Buybacks are value-destroying because companies tend to buy back their own stock when prices are too high

Reality 1.4: Buybacks, at any price, can neither add nor destroy value. They can just transfer value

    Warren Buffett was late to the buyback party, but when he initiated buybacks at Berkshire Hathaway, he introduced a constraint, which is that he would do buybacks only if he believed that the company's stock price was less than intrinsic value. He, of course, had the credibility to make this assertion, but most companies don't impose this constraint and there is evidence that they often buy back their shares when stock prices are higher than they are lower. That does seem like value destruction, but a cash return can neither add nor destroy value, but it can transfer wealth. In the case of stock buybacks at too high a price, wealth is transferred from those who remain loyal shareholders in the firm to those who sell their shares. While there is hand wringing about this, you have a choice, as a shareholder, in a buyback, to sell or hold on, and if you believe that the buyback is at too high a price, you should sell your shares back.

2. Myths in favor of the argument that buybacks are good and generate excess returns for investors 

Myth 2.1: Buybacks are value-adding because companies that buy back their own stock when prices are lower than fair value are taking positive net present value investments.

Reality 2.1: Buybacks, at any price, can neither add nor destroy value. They can just transfer value.

    This is the inverse of the argument that buybacks are value destroying and they are both grounded in a misclassification of buybacks as projects, rather than cash return, competing with investment projects for the company's dollars. The truth again is that a stock that buys back stock at lower than fair value is transferring wealth from those who sell back to those who remain, and here again, if you are on the wrong side of wealth transfer, it was your choice to sell back that made you the loser. 

Myth 2.2:  Buybacks are almost always good for stock prices, since there are fewer shares outstanding after buybacks, and that should increase the price per share.

Reality 2.2: A buyback can increase, do nothing or decrease value per share, depending on the price at which it is done and its effects on leverage.

    Buybacks reduce share count (the denominator) but the cash that leaves the firm also reduces fir value (the numerator). A fair-value buyback will create offsetting effects, leaving value per share unchanged, though there can be a secondary effect on value, if the buyback, by reducing equity, changes the debt to capital mix and cost of capital for a company:

It is true that empirical evidence backs up the notion that stock prices benefit from buybacks, but that may be from the selection bias of under levered firms with large cash balances being the biggest players in the stock buyback game. 

In general, almost all of these myths come out of treating buybacks as something new and different, rather than a variant on dividends. In general, companies that should not be paying dividends, either because they lack the cash or the future is uncertain, should not be buying back stock either. 

Dividend Dysfunction

    At the start of this post, I noted that dividend policy is dysfunctional at many firms, driven by inertia (we've always paid dividends or we've never paid dividend before) and the desire to hew to peer group policies. As a result, there are many companies around the world that adopt dividend policies that, at least of the face of it, take explaining including:

1. Money-losing companies that pay dividends: While there are some companies that offer justifications grounded in worries about sending bad signals or hopes of a bounce back in earnings, many get stuck with dividend policies, because of inertia or peer group pressure, that can drive them into ruin.
2. Money-making companies that refuse to pay dividends: Here again, there can be good reasons for holding back including concerns about whether you can sustain earning and expectations that you will need to invest more in the future, but in some cases, it can unwillingness to initiate dividends in an industry where no one else pays dividends.
3. Negative FCFE companies that return cash (dividends or buybacks): In addition to hopes for a bounce back in FCFE, companies may continue to return cash, even with negative FCFE, because they are trying to increase debt ratios or shrink their businesses over time. 
4. Positive FCFE companies that return no cash: Companies that have positive FCFE that don't return cash may hold back that cash because of the desire to reduce debt ratios or because they ahve investment plans.

The graph below lists out the number of companies in each group, broken down by geography:

Across the globe in 2025, almost 18% of money-losing companies paid dividends, as did about 70% of money-making companies. With FCFE as your indicator, about 37% of companies that returned cash (in dividends and buybacks) in 2025, had negative FCFE, as did 66% of companies with positive FCFE.

Conclusion

    There are a whole host of misalignments between what companies return to their shareholders, either as dividends or in buybacks, and what they can, as potential dividends. That suggests to me, and perhaps I am wrong, that investment strategies that are built around cash return, whether they be dividends or buybacks, are likely to go off the tracks. Furthermore, any strategy that is built entirely around dividends, as is the case with strategies where you load up on high dividend yield stocks or buy a handful of heavy dividend payers, such as the Dogs of the Dow, misses the essence of equity investing. A stock is not a bond, where dividends replace coupons, and you get some price appreciation on top, and treating it as such will only create disappointment.

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Data links

1. Dividend statistics, by industry (US and Global)
2. Buyback statistics, by industry (US and Global)
3. Dividends and Buybacks - History for US firms

Spreadsheets

1. Buyback stock price calculator

Data Update Posts for 2026

1. Data Update 1 for 2026: The Push and Pull of Data
2. Data Update 2 for 2026: Equities get tested and pass again!
3. Data Update 3 for 2026: The Trust Deficit - Bonds, Currencies, Gold and Bitcoin!
4. Data Update 4 for 2026: The Global Perspective
5. Data Update 5 for 2026: Risk and Hurdle Rates
6. Data Update 6 for 2026: In Search of Profitability
7. Data Update 7 for 2026: Debt and Taxes
8. Data Update 8 for 2026: Dividends and Buybacks