Data Update 6 for 2026: In Search of Profitability!

     Crass and mercantile though this may sound, the end game for a business is to make money, and a business that fails this simple test cannot survive for long, no matter how noble its social mission, how great its products and how much it is loved by its customers and employees. In this post, I start with a defense of this mercantile objective, and argue that attempts to expand it to incorporate social good leave both businesses and societies worse off.  I look at business profitability, first in absolute terms in 2025, and then relative to revenues, examining why profit margins vary across businesses and sectors. I then raise the ante and argue that making money is too low a standard to hold companies to, since the capital invested in these companies can generate returns elsewhere, opening the door to bringing in the opportunity costs (costs of equity and capital) that I introduced in my last post. 

The Business End Game

    In 1970, Milton Friedman argued in a New York Times article that the social responsibility of a business is to deliver (and increase) profits. That view has come under attack in recent decades, but even in the immediate aftermath of the article’s appearance, there was some push back. Some came from people who argued that Friedman was missing details, with a few noting that it is cashflows, not earnings, that businesses should focus on, and others arguing that it is profits over the long term, not just immediate profits, that should be the focus of a business. My guess is that Professor Friedman would have agreed on both fronts, arguing that he was talking about economic, not accounting, profits, and that there was nothing in his mission statement that foreclosed a focus on long term profits.

    In the decades since, there has been a more fundamental critique of the Friedman business end game, coming from those who believe that his view is far too cramped and narrow a vision for a business, and that businesses have obligations to society and the planet that need to be incorporated into decision-making. Initially, these critics argued for imposing social and environmental constraints on the profitability objective, and while Friedman may have taken issue with some of these constraints, arguing that that is what laws and regulations should be doing, he would (probably) have gone along with most of them, given real world frictions. Later, though, these critics decided to go for the jugular, arguing that the business objective itself be reframed to include these broader responsibilities, with some arguing for stakeholder wealth maximization, where businesses seek to maximize value to their different stakeholders (employees, lenders, customers). That idea gained traction among some academics, many of whom never grappled with putting this objective into practice in real businesses, and among some CEOs, who realized that being accountable to everyone effectively meant being accountable to no one, but I am not a fan.  About two decades ago, stakeholder wealth maximization was supplemented by ESG, an acronym that quickly got buy-in from the establishment. In 2020, when I first looked at ESG, it was at the height of its allure, with investment managers (led by Blackrock), consultants (with McKinsey up front) and academics, all pushing for its adoption. Given the broad buy in, I expected to see clear and conclusive evidence that ESG was not just good for investors and businesses, but also for society, and I was disappointed on every front. The alpha that was attributed to ESG in investing was accidental, coming almost entirely from its overload on tech stocks in its early years, the evidence that ESG helped businesses deliver higher growth and profits was laughably weak, and on almost every societal dimension that ESG was supposed to make the world a better place, it had failed. Even on risk, the one dimension where a rational argument can be mounted for companies following the ESG rulebook, its impact was hazy, with no discernible effects on costs of capital and only anecdotal (and mostly ex-post) evidence for protecting against reputational and catastrophic risks. In the last five years, ESG has fallen out of favor, largely undone by its own internal inconsistencies, but the gravy train that lived off its largesse has moved on, and taken much of what filled the ESG space, repackaged it, and renamed it sustainability. While advocates for sustainability try to create distance between ESG and sustainability, in my (biased) view, much of that discussion is akin to painting lipstick on a pig and then debating what shade of lipstick suits the pig best, rather than attempting to create real change.

    It is with intent, therefor, that I named these three forces - stakeholder wealth maximization, ESG and sustainability - the theocratic trifecta in a post that I wrote three years ago, and argued that they failed for the same reasons.

First, by rooting themselves in virtue rather than in business sense, they rendered a disservice to their own cause. After all, once you decide that you are on the side of goodness, any critics of what you do, no matter how well merited their criticism might be, are quickly consigned to the badness heap, and not just ignored, but also reviled for lacking moral fibre. The problem, of course, is that if an action makes business sense (increases profitability and value), you would not need a virtue brigade to push for that action in the first place. Second, by leaving the definitions of their central ideas (stakeholder wealth, ESG and sustainability) amorphous, they made it easier to sell to investors and companies, but at the expense of consistency and focus. In my 2022 post on ESG, where the Russian invasion of Ukraine had forced its defenders to morph in the face of evidence that that world was more dependent on fossil fuels and defense companies than they had been willing to concede in earlier years, I noted the loss of credibility that comes from shifting definitions of goodness. Third, and most critically, in their zeal to push these concepts to a wider audience and get more people to buy in, they sold a lie, i.e., that you can be good (whatever that definition of good may be) without sacrifice. I have no idea whether ESG and sustainability salespeople meant what they said when they argued that investors could earn higher returns, by adding ESG constraints to their portfolios, and that companies could become more profitable, if they incorporated environmental and social considerations into decision making, but my categorization of people in these spaces as either useful idiots or feckless knaves stems from a refusal to face up to the inherent trade offs.

    After decades of pushback from critics of the Friedman business end game, I, for one, believe that Milton Friedman was right, and that we would all be better off to follow up and ask the question of what can be done, given that businesses are profit-seekers, to advance social good and curb externalities. I don't believe that the disclosure route, which seems to have become the fallback for some seeking better business behavior, will accomplish much, and it may do more harm than good. While laws and regulations can provide a partial fix, they are blunt instruments, and in a setting where businesses can move easily across borders, they may not be effective. Ultimately, we (as consumers and voter) get the businesses we deserve, and if after paying lip service to social causes, we buy products and vote for governments hat undercut those causes, no acronym or word salad will repair the breach.

Profitability in Businesses

    I meant to have a short lead-in on why profitability matters at businesses, but as you can see from the previous section, I did get side tracked, but the underlying message is that making money is central to business success and survival, and that measuring profitability is therefore a necessary part of assessing business success and value. 

Economic versus Accounting Profits

    The Friedman view on the business endgame may have been driven by a vision of economic profits, but in the real world, we are dependent on accounting measures of profits, which are, at best, imperfect substitutes for economic profits. The table below looks at an accounting income statement, highlighting the many measures of profits - gross, operating and net - that you will find in it:

Each profit measure has utility, with gross profits reflecting unit economics, the difference between gross and operating profits capturing economies of scale and the difference between operating and net profits being driven by taxes and choices that businesses make on debt and non-operating assets. In 2025, looking at the aggregate values (in millions of US $) for these line items across sectors, here are the numbers, for both global firms and just the US subset:

In the aggregate, global firms generated $6.2 trillion in net income and $7.7 trillion in operating income on revenues of $72.4 trillion, in 2025; during the same year, US firms generated $2.2 trillion in net income and $2.9 trillion in operating income on revenues of $22.7 trillion in revenues. Across sectors, and looking at revenues, industrials carried the most weight for the global sample, but health care generated the most revenues across the US sub-sample.

Profits scaled to Revenues - Profit Margins

    The problem with dollar profits is that comparisons across companies, industries or sectors are skewed by scale differences, and one simple scalar for earnings is revenues, yielding variants of profit margins. While you are undoubtedly familiar with these margin variants, their real use in analysis is in providing insight into business models

I am not a believer in financial ratio analysis, but I do believe that the income statements for companies, especially examined over time, give us insight into their business models and can help frame valuation narratives. In the table below, I look at differences in margins across sectors in 2025, again looking across global firms, and just US firms:

I have estimated margins, by sector, using the aggregated dollar values for profits and revenues from the previous table, and also reported the cross sectional distribution of company-level margins. Comparing the aggregated margin with the median margin across the sector should give you a sense of how top-heavy the sector is in terms of profitability. In technology, which has the highest weighted operating margin (24.7%) of across sectors, the median operating margin is only 3.41% (-0.30%) across global (US) technology firms; the bigger tech companies are money machines in a sector that still contains a lot of younger and smaller money-losing firms. Note that the margins are not computed for financial service firms, since revenues are often unreported (and mostly meaningless) and gross and operating profits don't have the same measurement value as they do for non-financial service firms.

Industry Margins and the AI Threat

    Breaking down sectors into industries provides more granular detail, and there is a link at the bottom of this post that reports the margin statistics, by industry group. At the risk of stating the obvious, there are large disparities on margins across industries, reflecting differences in unit economics, economies of scale and leverage, as can be seen in this table that lists the industry groupings with the highest and lowest aggregated operating margins among US firms:

At one end of the spectrum, you have industry groups like basic chemicals, which has an aggregated (median) gross margin of 9.31%, making the margin hill much steeper to climb, since operating margins and net margins will be lower. At the other end of the spectrum, in addition to tobacco and railroads (surprised, right?), you have system and application software, delivering an aggregated gross margin of 71.72%, operating margin of 33.21% and net margins of 25.49%, capturing the strong unit economics that characterize the business. 

    While high margins are a desirable feature for a business, these same high margins can make a business vulnerable to disruption, and the AI sell off that we have seen play out in the last few months in software reflects the concerns that investors have of AI putting significant downward pressure on software margins. If your pushback is that the drop off in revenues and margins has not happened yet, and that it is unfair to software firms to mark their market pricing down preemptively, this is exactly what markets are supposed to do, and these software companies benefited earlier in their lives, when market prices were marked up well ahead of the run-up in margins. You live by the sword (expectations of growth and high margins), you die by it (expectations that growth rates will hit a cliff and margins will decline)!

Time Trends in Profits

    I have tracked profit margins for companies for a long time (about three decades) in my datasets, and there is clear evidences that they have trended upwards during the period. In the graph below, I look at the net profit margins for the S&P 500 in the aggregate in this century (from 2000-2025):

As you can see, net profit margins have climbed over the last two decades for US companies, with a number of stories competing for why.  

• The most cynical explanation is that this increase in margins is all sleight-of-hand, where accountants are pushing through changes, aided and abetted by accounting rule-writers, to make companies look more profitable. As someone who has taken issue with the gaming of earnings that you often see at companies, I am disinclined to take this criticism seriously, since many of the changes in accounting rules (such as the expensing of stock-based compensation and R&D) should push earnings down, and accountants have more power to move income across periods than they do to increase the level of income.
• A second explanation is that the macroeconomic environment makes it easier for companies to deliver profits, and this explanation had resonance when interest rates were at historic lows in the last decade. As rates have risen back to more normal levels and the economy limps along, I am skeptical of the reasoning in this explanation.
• A third explanation, and this one has been eagerly adopted by many on the political left, is that that this reflects the increase in bargaining power for capital, relative especially to labor, implying that the increase in profits are coming primarily at the expense of worker wages. While there are certainly pockets of the economy where this is true, the margins for most manufacturing and service businesses, which have the highest employee count and wage costs, have stagnated or decreased over the last 20 years, indicating that neither capital nor labor has benefited at least in these sectors.
• The fourth, and in my view the most salient rationale for margin increases, is that the composition of the market has changed, as technology companies supplant old-economy companies, bringing superior unit economics and economies of scale to play. Put simple, a market that gets the largest portion of its value from tech companies will deliver much higher margins that one that gets much of its value from manufacturing and service businesses.

Should we concerned that margins may compress in the future? Of course, and we always should, but that compression, if it happens, will depend almost entirely on how the economy performs and the effects of disruption, if it is coming, for tech companies. 

Value Creation in Business

    If we define the threshold for business success as generating profits, we are setting the bar too low for a simple reason. Starting a business requires capital, and that capital can earn a return elsewhere on investment of equivalent risk. If those words sounds familiar, it is because I used them in my last post on hurdle rates to describe the costs of equity and capital. Thus, value creation requires a business to generate a return on its equity (capital) that exceeds its cost of equity (capital). That is a simple proposition, and a powerful one, but the measurement challenge we face is in determining the returns that companies generate, and for better or worse, we are dependent on accounting measures of these returns. A good way to see what an accounting return is measuring or at least trying to measure is to look at returns on equity and invested capital in a financial balance sheet:

While accounting returns are widely used in practice, as a gauge of investment quality, they can be skewed not just by accounting inconsistencies but efforts by accountants to do the "right thing" (like writing off bad investments. I have laid out my concerns in exhaustive and incredibly boring detail in this paper on accounting returns, which is dated, but still relevant. I summarize the factors that can cause accounting returns on equity and capital to deviate from reality in the picture below:

    With those concerns about accounting returns in place, I computed the accounting returns on equity and invested capital for all of the companies in my global sample (48.156 firms) and my US sample (5994 firms), and the following table reports the statistics for both groups, by sector:

Again, I report the accounting returns computed based on aggregated values first, and then the distributional statistics (first quartile, median, third quartile) for the company-level accounting returns. As with profit margins, you can see that even in sectors where the aggregated accounting returns are high (such as technology and communication services), the median value reflects the reality that most companies in these sectors struggle to deliver double-digit returns.

    Turning back to our value creation metric, where we compare accounting returns to costs of equity and capital, you have to be consistent, comparing equity returns to equity costs and capital returns to capital costs:

The excess return is a numeric, but as with all numbers in business, it is worth looking behind the number at its drivers, i.e., why do some business deliver returns that consistently outstrip their costs of equity and capital, whereas others struggle? The most powerful explainer of excess returns is not qualitative, since the capacity to generate excess returns comes from barriers to entry and competitive advantages. In the language of value investing, it is the width (strength of competitive advantages) and depth (sustainability of competitive advantage) of moats that determine whether a company can earn more than its cost of equity or capital:

If you are interested in this topic, and it is a fascinating one, Michael Mauboussin brings his erudition and knowledge into play in  this Morgan Stanley thought piece from October 2024.

    Since I have estimates of costs of equity and capital for each of my firms (see my last data update for details), I compute excess returns, by sector, for my global and US samples:

Given what you saw in the last table, with accounting returns, you should not be surprised to learn that only 29% (28%) of global firms earn returns on equity (capital) that exceed their costs of equity (capital). In fact, if you raise the threshold and look at companies that generate 5% or more as excess returns, the numbers drop off to 19% (17%) for equity (capital) excess returns. Most companies have trouble earning their costs of equity and capital, but if you look at the aggregated values, there are multiple sectors in the US (technology, consumer goods and communication services) that earn double digit excess returns, pointing again to larger companies within these sectors being able to set themselves apart from the rest.

    If your concern is that the global statistics are being skewed by regional differences, I compute the excess return statistics broken down by region:

As you can see, there is not a single geography where more than 50% of firms earn more than their required returns, with Japan ranking highest in percentages and Canada and Australia the lowest. Here again, the aggregated values tell a different story, with US companies collectively delivering excess returns of 8.44% on equity and 1.81% on capital, suggesting again that large US companies carry the weight of value creation in the market.

    Given how much time we spend in finance examining investments and developing decision rules (NPV>0, IRR>Hurdle rate) that are supposed to protect businesses from taking "bad" investments, you may be surprised at the prevalence of value destroying investments. Some of the failure at businesses to deliver returns on capital that exceed the cost of capital may reflect imperfections in our accounting return measures, since it is based upon earnings in the most recent year, and that may bias us against young and growing companies building up to scale. In my book on corporate life cycle, I highlight how accounting returns shift as companies go from youth to decline:

To see if this is a factor in our global findings on excess returns, I break companies down by age into deciles and compute excess returns across these groupings:

The table broadly reflects what you should expect to see, with a corporate life cycle, as the percent of companies that beat their cost of capital increase as companies age, but the aggregated excess returns peak in middle age (the middle of the life cycle), more pronounced with US than global firms.

A Profitability Wrap Up

    Looking at the data, and there is a danger here that I am overreaching, it seems to me that over the last four decades, moats have crumbled, partly as a result of global competition and partly because of disruption (which upends businesses, turning good businesses to bad ones), and the business landscape has tilted more decisively to larger firms, as more and more businesses become winner(s)-take-all. It is in this context that I take a more jaundiced view of what AI will do for company profitability and value. I believe that, as a disruptor, it will cause downward pressure on margins at most firms, and increase the advantages that larger firms have in each business. How do I reconcile this view with the happy talk of AI as a tool that will make companies more productive, and that the resulting lower costs will make them more profitable? Unless the AI tools that you are talking about are exclusive to these companies, in the sense that competitors cannot buy the same or equivalent tools, these AI tools will lower costs across the board, and competition will then kick in on the pricing front, lowering profitability. If that sounds like a reach, I would recommend a revisit of the US retail sector over the last three decades, as online retail, initially viewed as a boon by brick-and-mortar retail firms, ended up destroying most of them and reducing the margins for retail collectively. As consumers, we will benefit, but as investors or employees in the disrupted companies, we will pay a price that outweigh the benefits, for a sizable number of us. I do think that the AI disruption will be more akin to a slow-motion car wreck, in terms of its effect on overall profitability, and that the margin slippage will occur over time, but it will damaging. Time will tell!

YouTube Video

Datasets

1. Profit margins, by industry (US and Global)
2. Accounting returns and excess returns, by industry (US and Global)

Paper on Accounting Returns (Long and Boring)

1. Return on Capital, Return on Invested Capital and Return on Equity: Measurement and Implications

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

Data Update 5 for 2026: Risk and Hurdle Rates

    In my first four posts, I looked at markets - equity, debt and collectibles - in the aggregate performed in 2025. In this post, I turn my attention to divergences in risk across companies, looking at alternative measures of risk, some based on prices and others at earnings, and how these differences play out in hurdle rates, a necessary ingredient for businesses trying to determine whether and how much to invest in individual projects and for investors making that same judgment, when looking at companies.

Risk: Definition and Measures

    For a concept tas central to investing and corporate finance as risk is, it is astonishing how much divergence there is across even finance experts and academics on what it is, and consequently on how to measure it. I have heard some describe risk as uncertainty, essentially substituting one fuzzy word for another, others as the threat of grevious loss and still and still others as the possibility of negative outcomes. If you have taken a finance class, and I confess to having a part in this, you may define risk as volatility or standard deviation, or even bring Greek alphabets into play. My favorite definition of risk and one that I start my corporate finance class with is that Chinese symbol for crisis or big risk (and I am sure that I have mangled the symbols, since I have been corrected a dozen times in the past):

As someone who can neither read nor speak Chinese, I am reliant on friends who know the language, and I have been told that the first of the two symbols is the one for danger and the second is a symbol for opportunity. In effect, by bunding together danger and opportunity, the risk measure captures how risk both attracts (to get to opportunity) and repels (with the threat of danger). That duality explains why an investment or business strategy generally cannot be built around the objective of just minimizing risk, since that effectively will remove access to opportunities or recklessly chasing after opportunities, ignoring dangers 

    With that definition of risk in place, I will start the discussion of risk measures by examining the choices that we face in making the measurement:

1. Upside versus Downside: If you start with a generic definition of risk as receiving an outcome that is different from what your expectation, it is worth recognizing that some of these outcomes will be positive (better than expected) and some will be negative (worse than expected), and that it is the latter than investors and businesses dislike. Thus, there are some who argue that risk measures should focus on just downside outcomes, not all unexpected outcome.
2. Price-based versus accounting-based: Risk measures that are based upon data can be built on market prices, for publicly traded firms, or on accounting data, especially earnings. Price-based measures have the advantage of constant updating, giving you more data, but are sometimes contaminated by the noise and volatility that come from trading. Accounting measures yield more stability, but since they are updated infrequently, and accounting smooths changes over time, they can offer stale or distorted values.
3. Total versus Non-diversifiable: The risk in an investment, whether a project or a business, can come from many different sources, but some of the risks are more investment-specific whereas others are market-wide:

To the question of why we should care, the presence of many investments in a portfolio implies that risks that are investment-specific will average out, decreasing or even disappearing as portfolios get larger, whereas market risks remain intact. This insight, which earned Harry Markowitz a Nobel prize, gave birth to modern portfolio theory and is at the heart of most risk and return models in finance.

    I have my preferences on how best to measure risk, I would like to keep an open mind and start by laying out the choices we face on risk-measures:

As you can see, the risk measure you choose will be a function of whether you (as an investor or business) believe that the marginal investors, i.e., the investors who own the most shares in your business and trade those share, are diversified or not, and what you believe about financial markets and accounting data.

Risk across Companies in 2025

    My sample includes 48,156 publicly traded firms and given that these companies trade across different geographies and are in different businesses, it should come as no surprise that there are wide variations in risk across these companies. In this section, I will start with accounting-based measures, with the caveat that accounting standards vary across the world, though IFRS and GAAP have created significant convergence. 

Accounting Measures

    While there are a variety of accounting metrics that you can use to measure risk, the most logical one to focus on is earnings, but you have many choices. You could use net income or earnings per share, which will reflect not only the riskiness of the business operate in, but also the amount of debt you have chosen to take on, or you can used operating income, more reflective of just market risk. Within each of these metrics, you can measure risk as volatility (in earnings) or in more simplistic terms, on whether you have positive or negative income. For those investors and businesses to whom, it is debt that is the risk trigger, you can look at measures of that debt burden:

Let’s start with volatility in earnings, where we have two estimation choices that we must make, before we get started. The first is history, and I compute the standard deviations in operating and net income using ten years of earnings data, for each firm, a compromise between a number too high (where I lose too many firms in my sample) and too low (where I lack enough data). The second is that earnings standard deviations in earnings will reflect the level of earnings, with higher earnings companies having higher standard deviations. To control for this, I divide the standard deviation of earnings by the average earnings over the ten years, yielding coefficients of variation in earnings. The following table summarizes the distributional values for this metric, across sectors:

It should come as no surprise that utilities have the least volatile operating earnings and have the lowest coefficient of variation on that metric, and that energy and technology haver the most volatile operating income. On a net income basis, financials and utilities have the lowest volatility in earnings, , and energy and communication services have the highest net income volatility.

    If you use the frequency of loss-making, as a risk proxy, the table below captures differences on that metric across sectors on this dimension:

Utilities are again the least risky sector, with a lower percentage of money losers than any other sector, and health care and technology firms have a higher percent of money losers than other sectors.

    While there are some who use debt loads as proxies for company risk, and we will come back and look at differences across sectors and industries in a later post, it is a narrow measure, since a young, risky, high growth company with no debt would be classified as low-risk, if it is not debt-laden.

 
Price-based Measures

    All of the stocks in our sample are publicly traded, and consequently, you can use market prices to measure risk. That said, liquidity is a wild card, high in some markets and low in others, and that can cause distortions in the comparison.

1. High and Low Prices: One of the simplest measures of price volatility is the range of prices, with wider divergences between high and low prices at more risky companies and smaller ones at safer companies:

HiLo Risk Measure = (High Price – Low Price)/ (High Price + Low Price)

I computed this statistic for each company in my sample, and then the averages across companies in each industry, and it should be lower (higher) for safer (riskier) stocks.  Using my global data, this is what this statistic looks like, across sectors:

Utilities again come in as safest, using this risk metric, tied with real estate, and health care has the widest price ranges of the companies in my sample.

2. Standard deviation in price changes: This is a standard statistical construct, and measures volatility in a stock, though it does not distinguish between upside and downside volatility. Based upon the company-specific standard deviations, again averaged out across sectors, here is what the numbers looked like in 2025:

Financials and utilities are the two safest sectors, and technology and health care are the riskiest, if you measure risk with standard deviation.

3. Betas: If you buy into the notion that the investors setting prices are diversified, and thus care only about risk that cannot be diversified away, you will focus only on the portion of the standard deviation in a stock that comes from the market, and betas, notwithstanding the misinterpretations and misreading, are trying to measure that non-diversifiable portion of standard deviation and scale around one. Again, looking across industries, I look at the distribution of betas, by sector:

If you are interested in a less broad categorization, you can check out betas by industry at the end of this post.

As you review the sector rankings using the varied risk measures, you can see why the heated debates about which risk measure to use is often overdone, since they, for the most part, rank the sectors similarly, with the sectors having less earnings volatility and fewer money-losers also having less volatility in stock price, smaller price ranges and lower betas.

Hurdle Rates
    Even as we wrestle with choosing between price and accounting-based measures, it is worth remembering that the end game here is not the risk measure itself, and that risk measures are a means to an end, which is estimating hurdle rates. Hurdle rates come into play for both businesses and investors, setting thresholds that they can use to determine whether to invest or not:

There are some investors and businesses who believe that hurdle rates come from their guts, numbers that reflect personal risk aversion and past experiences, but hurdle rates are opportunity costs, reflecting returns that investors (businesses) can earn in the market on investments of equivalent risk. 

    In the context of a business, which raises money from debt and equity, you can look at hurdle rates through the eyes of the capital providers – a cost of equity, capturing what equity investor believers expect to make on other equity investments of equivalent risk, and a cost of debt, looking at what lenders can earn on lending to others with similar default risk:

That is what all risk and return models try to do, albeit with different degrees of fidelity to the principle. In fact, my use of an implied equity risk premium in the estimation of the cost of equity is designed to advance this cause, since it is model-agnostic and reflects what investors are pricing stocks to earn, on an annual basis. Thus, when you use the beta in the capital asset pricing model to derive the cost of equity, you should be computing the return you can earn elsewhere in the market on other investments with the same beta, making the cost of equity the hurdle rate for equity investments in a project or company. The cost of capital, which incorporate the cost of borrowing into its construct, is also a hurdle rate, albeit to both debt and equity providers:

As to the question of which of these hurdle rates you should use as a business, the answer lies in consistence. If you are looking at equity returns (return on equity or an internal rate of return based on equity cash flows alone), you should be measuring up against just the cost of equity. Alternatively, with returns on invested capital or an internal rate of return based upon cashflows to the business (pre-debt), it is the cost of capital that comes into play.

I compute the costs of equity and capital for all 48,156 firms in my sample, and in doing so, and in the interests of consistency and ease, I make some simplifying assumptions:

Once I have the costs of equity and capital for each firm, I compute industry averages, both for global firms, and by region (US, Japan, Europe, Emerging Markets, with India and China as sub-categories). You can find the links to the data at the end of this post, but there is another perspective that you can bring to the cost of capital discussion, based upon where a company falls in the company life cycle:

Intuitively, you would expect more uncertainty about business prospects with younger firms, than older ones, especially on the estimation front. That said, it is an open question of whether this uncertainty will translate into higher costs of equity and capital, since it depends on who the marginal investors in these firms are, and whether the risk is diversifiable (and not affect cost of equity) or non-diversifiable. To answer these questions, I classify firms into ten deciles, based on their corporate age, and compute costs of capital:

As you can see, there is no discernible pattern on costs of equity, as you go across the age classes. However, as firms age, they do borrow more, partly because their capacity to generate earnings increase, and that does have some impact on the cost of capital, especially with the oldest firms in the market.
    In corporate finance and valuation, an undervalued skill is having perspective, a sense of what comprises typical, and what is a high or a low value. It is for that reason that I also compute a histogram of costs of capital of all publicly traded firms at the start of 2026:

This table is one on my most-used, for many reasons. First, when doing my own valuations, especially for young firms or for firms where the cost of capital is in flux, it gives me the input to us. Thus, if I am valuing a small, AI firm that has just gone public and has global operations, in US dollars, I will start the valuation with a cost of capital of 11.66% and move that cost of capital over time towards 8.65%, as its gets larger and more established. Second, I do see (and must review or grade) other people’s valuations more than I do my own, and this table operates as a plausibility check; a valuation of a publicly traded US company that has a dollar cost of capital of 14% goes on my suspect list, since that is well above the 90th percentile for US firms. Third, the table operates as a reminder that any analysts where the bulk of the time is spent estimating and finessing the cost of capital is time ill-spent, since the 80% of all US (global) companies have costs of capital between 5.26% (6.28%) and 9.88% (11.66%).

    For those working in different currencies, the inflation differential approach that I described and used in the last post can be used to convert the entire table. Thus, if you use the expected inflation rates of 2.24% and 4.00% for the United States and India, from the IMF forecasts, you can 1.76% to each of the numbers to each dollar cost of capital that you see in the table or as an industry average.

Conclusion

    To run a business or invest in one, you need hurdle rates, and that is what costs of equi6y and debt measure. While models and equations may be how you get these numbers, it is always worth going back to first principles, whenever you face questions on what to do. Thus, recognizing that the cost of capital is an opportunity cost, i.e., the rate of return you can earn elsewhere in the market, on investments of equivalent risk, should be a prompt to use betas that reflect the risk in investments, rather than the entities making the investment, and updated costs of borrowing for the cost of debt. As we enter 2026, we are now in our fourth year with US dollar riskfree rates around 4%, and companies and investors seem to have become acclimatized to the resulting costs of capital, and the shock of seeing dollar riskfree rates surge in 2022, pushing up costs of capital across the board seem to have faded.

YouTube


Datasets

1. Earnings variability, by industry (Global in 2025)
2. Money making and losing percentages, by industry (Global in 2025)
3. Pricing risk measures, by industry (Global in 2025)
4. Betas by industry group (US, Global, Japan, Europe, Emerging Markets, India & China)
5. Cost of capital by industry group (US, Global, Japan, Europe, Emerging Markets, India & China)

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

Data Update 4 for 2026: The Global Perspective!

    If you have read my first three data updates in 2026, I won’t blame you if you skip this one, because you found them long and boring. I won't take issue with you either if you viewed them as too US-focused, because I did spend my second data update, looking at US equities, and my third, examining US treasuries and the US dollar. In this post, I widen my data analysis to look at the rest of the world, starting with a journey through global equity markets in 2025, moving on to creating a snapshot of country risk at the start of 2025 and finishing by looking at interest rate differences across currencies. Along the way, I will argue for a larger narrative, underlying this global perspective. I am not a political or a macroeconomic analyst, but I attribute much of what we have seen in terms of global politics and economics in the last four decades, first to the rise of globalization as an almost unstoppable force, shaping immigration and economic policies in much of the world, and then, in most recent years, to a backlash against the same forces. That backlash has not only upended the political order in the developed world, with both Europe and the United States seeing changes in power structure, but also brought nationalist parties to power in many emerging market countries. From investing and business perspectives, we saw the effects play out strongly in 2025, and I don't think that this genie is going back into the bottle.

Global Equties in 2025

    In my second data update, I noted that US equities had a good year in 2025, delivering a return of 17.72% for the year, but the US dollar weakened in 2025, down a bit more than 7% during the year. I started my exploration of global equities by looking at the returns in local currency terms of equity indices in different parts of the world:

 

In each region, I have highlighted the best performing index (in green) and worst performing one (in red), and you can see the disparities in market performance, even within regions. One of the problems with comparing returns across currencies is that they are distorted by the effects of inflation that also vary widely across currencies. While I will look at inflation differences in more detail later in this post, one way to make the returns comparable is to recompute them in a common currency. To this end, I compute the dollar returns, in aggregate dollar market capitalization terms, in 2025:

As I mentioned in my second data update, India was the worst performing sub-region of the world, up only 3.31% in 2025, and those returns reflect not just a relatively below-average year in local currency terms, with the Sensex up 8.55% for the year, but a weaker currency, with the rupee depreciating against the dollar. It is only one year and while I will need read too much into it, my argument earlier last year that the India story has legs, but that the path to delivering it will be rockier than many of its advocates seem to thing. For much of the rest of the world, the dollar returns are higher than local currency returns, because of currency appreciation against the dollar.

    Zeroing in on the aggregate market capitalization across the world at the start of 2026, I first created a pie chart (on the left)  breaking market capitalization by region, and as you can see, US equities, in spite of a weaker dollar, accounted for 47% of global market capitalization.

Evaluating just the change in market capitalization during 2025, in the second pie (not he right), you can see the reason for the slippage in the US hare, with the US punching in below its weight (38% of the change) and Europe and China weighing in, with larger shares. 

  To close this section, I will unwrite an epitaph for international diversification that many US investors, wealth advisors and market experts were starting to etch in stone even a year ago. For much of the twenty first century, an investor invested entirely in US stocks would have outperformed one who followed the textbook advice to diversify globally. While that may look sound conclusive, the truth is that two decades is not a long time period in stock market history and that you can have extended market runs that look permanent, even when they are not. It is true that as multinationals displace domestic companies, the payoff to international diversification has become smaller over time; buying the S&P 500 would have bought your exposure to the global economy, since the companies in the index, while incorporated in the US, get almost 60% of their revenues in the rest of the world. However, the underperformance of the US, relative to the rest of the world, in 2025 should be a reminder that international diversification still belongs in the toolkit for a prudent investor. That lesson cuts across the globe, and suggests that much as politicians and countries may want to delink from each others, investors don't have that choice.

Country risk in 2025

    If you have been a reader of my posts, I do have a bit of an obsession with country risk,, i.e., why the risk of investing and doing business varies across countries, and what causes that risk to change. My defense for that is that I teach corporate finance and valuation, and to do either, I need answers to these country risk questions, and while you may not like the short cuts and approximations I use along the way, I will take you along on my January 2026 journey:

    The place to start any discussion of country risk is with an examination of the factors that feed into that risk, and I will use a matrix that you may have seen in my prior posts on country risk:

While I do take a deeper and more detailed look at these factors in a mid-year update that I do every year (links to paper and my July 2025 blog post), the forces that cause differences in country risk span politics and economics, and include:

1. Political Structure: From an investing and business standpoint, the choice between democracy and autocracy is nuanced, with the former creating more continuous uncertainty, as changes in government bring more policy change , and the latter creating more policy stability in the near term, albeit with a greater likelihood for wrenching and potentially catastrophic uncertainties over time.
2. War and Violence: Investing and business become more hazardous, both physically and economically, if you invest in a more violent setting, and war, terrorism and access to weapons can create differences across countries.
3. Corruption: Corruption affects businesses directly, operating as implicit taxes on businesses that are exposed to it, and indirectly, by undercutting trust and the willingness to follow rules. While differences in corruption across countries are often attributed to cultural factors, a significant component of corruption comes from structures that are designed to encourage and reward it.
4. Legal and Property rights: Investors and businesses are dependent on contracts and legal agreements to operate, but protection for property rights. Legal systems that are capricious in how they enforce contractual and ownership rights, or delay judgments to make them effectively useless, create risks for businesses and investors.

There are many reasons to expect differences across countries, on these dimensions, there is a different perspective that can also help. As some of you may know, I look at businesses through the lens of a corporate life cycle, where as businesses age, their characteristics and challenges change as well. That life cycle structure can be used to explain differences across countries, where the age is less tied to how long a country has been in being and more to do with its economy.

Young economies have higher growth potential, but that higher economic growth comes with more risk (more volatile economies) and require more robust governance to deliver on their promise. As economies age, they face a period of lower growth, albeit with more economic stability, and governance matters less, effectively become mature (middle aged) economies. There is a final phase, where a country’s economy hits walls, and growth can stagnate or even become negative, driven partly by a loss of competitive edge and partly by aging populations. In each of these phases, countries often overreach, with young countries aspiring for the stability of middle age, while trying to grow at double-digit rates, and mature companies, seeking to rediscover high growth. Without treading too much on political terrain, it may be worth thinking about the Trump actions in 2025 as driven, at least partially, by nostalgia for a different time, when the United States was the dominant economic power, with a combination of solid economic growth and stability that few economies, almost unmatched in history.

    With that philosophical discourse in country risk out of the way, let’s turn to the brass tacks of measuring country risk, starting with one of the most accessible and widely available one, which are ratings that agencies such as S&P, Moody’s and Fitch (among others) attach to sovereigns. The following is the heatmap of sovereign ratings (from Moody’s) at the start of 2026:

While Moody’s rates more than 140 countries, there remain a few (called frontier markets) that have no ratings, but in terms of the color map, I have included those countries with the lowest rated, because they share many of the same risk characteristics. There are three key features of these ratings that are worth emphasizing:

1. The sovereign ratings are focused almost entirely on default risk, and while the chance that a country will default is correlated with the core risks (violence, political structure, legal system and corruption) that I mentioned up front, there are countries on this list where they diverge. I believe that this is especially the case in the Middle East, where there are countries, like Saudi Arabia, that have low or no default risk, but remain exposed to large political risks.
2. The sovereign ratings have their share of biases, for or against regions, but their bigger sin is that they are slow to react. If you look at the list, you will see countries like Argentina and Venezuela that have seen significant changes in governance and politics in the last year, but where the ratings have not changed or barely changed. That will probably change in 2026, but this delayed response will mean that the sovereign ratings for some countries, at least, will not be good reflections of country risk, at the moment.
3. There were a few ratings changes in 2025, mostly at the margin, but the one that got the most attention was the ratings downgrade for the US that I highlighted at the time it happened. While markets, for the most part, took that ratings downgrade in stride, it did create waves in the process that I use to estimate riskfree rates and equity risk premiums, by country, as you will see later in this post.

The reason that so much of how we deal with country risk rests on sovereign ratings is not because ratings agencies have special insights, but because sovereign ratings, unlike other (often more comprehensive) measures of country risk, like country risk scores (from PRS or the Economist, to name two), can be converted into default spreads that conveniently feed into financial analysis. At the start of 2026, here are my estimates of default spreads for each sovereign rating:

As I noted earlier though, using sovereign ratings to get default spreads comes with the limitations that these ratings may not reflect current conditions, when change is rapid, and that is where the sovereign CDS market has created an alternative. For the 80 countries where sovereign CDS exist, you can get a market-determined number for the default spread, and here are the numbers at the start of 2026:

Note that these spreads, while noisy and reflective of market mood, reflect the world we live in, and both Argentina and Venezuela, which used to be uninsurable, have both seen improvement on these market-driven numbers, albeit from impossible to insure to really costly to insure.

    As a final step in my country risk exploration, I repeat a process that I have used to estimate equity risk premiums, by country, every six months for close to three decades. That process starts with estimating an equity risk premium for the S&P 500, and then uses the country default spreads (based upon the ratings) to estimate equity risk premiums for countries:

It is undeniable that the ratings downgrade for the US has created some change in this process. Instead of using the S&P 500’s implied equity risk premium as my estimate of the mature market premium, which was my pathway until May 2025, I now remove the default spread (0.23%) for the US from that premium to get to a mature market equity risk premium (4.23%). To get to country risk premiums for individual countries, I scale up the ratings-based default spreads for the relative riskiness of equities, and add these country risk premiums to the mature market premium:

Download equity risk premiums, by country

Note that I bring the frontier countries into the mix, by using country risk scores for these countries to estimate country and equity risk premiums. 

The Currency Effect

    While it remains true that country risk and currency volatility/devaluation often go together, one of my concerns with mixing up the two up is that you end up double counting or miscounting risk. To understand the divide between country and currency risk, I start with a look at government bond rates in different currencies, with the caveat that there only about forty governments that issue bonds in their local currencies and that some or many of these government bonds are lightly traded, making their rates unreliable.

In many finance classes and textbooks, you are often taught (as I was) to use the government bond rate as the riskfree rate, on the facile assumption that governments should not default on these bonds, since they can print more currency and cover their debt obligations. The problem with that logic is that it is at odds with the reality that governments can, and often do, default on local currency bonds, choosing that option over devaluation. That also means that the government bond rates can include a default risk component, and to get to a riskfree rate, that default risk needs to be removed from the government bond rate. In the picture above, that is what I do, using the ratings-based default spread). After this clean-up, you can see that riskfree rates vary widely across currencies, from very low in some currencies (Swiss Franc, Japanese yen and the Thai Baht), slightly higher for others (US dollar, Euros) and very high on a few (Turkish Lira, Zambian kwacha). 

    In my third data update, I estimated an intrinsic riskfree rate for the US dollar, by adding inflation and real GDP growth. Extending that lesson to other currencies, the primary reason for differences in these riskfree rates, across currencies, is expected inflation, with higher(lower) interest rates in higher (lower) inflation currencies. While inflation measures are imperfect and expected inflation estimates are often flawed, I use the IMF’s estimates of inflation to build a global inflation heat map:

The logic that I used to argue that it is unlikely that you will see US treasury bond rates drop much below 4%, at least as long as inflation runs hot (2.5-3%), not only applies for other currencies, but yields a roadmap for estimating riskfree rates in those currencies (including those without a government bond in the local currency). To illustrate, I will try to estimate an Egyptian pound riskfree rate at the start of 2026:

Riskfree rate in local currency = Riskfree rate in US dollars + (Expected inflation rate in local currency – Expected inflation in US $)

Thus, the riskfree rate in Egyptian pounds, using the expected inflation rates of 7.78% for Egypt and 2.24% for the United States is 9.49%:

          Riskfree rate in US dollars = US T.Bond rate - US default spread = 4.18% -0.23% = 3.95%

Riskfree rate in EGP (1/1/26) = Riskfree rate in US $ + (Expected inflation in Egypt – Expected inflation in US) = 3.95% + (7.78% - 2.24%) = 9.49%

Note that the riskfree rate in US $ is 3.95%, obtained by cleansing the US 10-year treasury rate on January 1, 2026 (4.18%) of US default risk (0.23%). The estimate for a riskfree rate is an approximation is an approximation, since inflation rates compound, and that compounded version is below:

Riskfree rate in EGP = (1+ US $ Riskfree Rate) × (1 + Expected inflation rate in EGP)/ (1+ Expected inflation rate in US $) -1 = 1.0395 × (1.0778/ 1.0224) -1 = .0958 or 9.58%

I have used IMF inflation rates to get riskfree rates in almost all global currencies in this link, but I don’t blame you, if you are skeptical about the expected inflation numbers. From a financial analysis and valuation perspective, I have good news and it is that it does not matter if you are wrong on inflation, if you are consistently so (in both your earnings and cash flows as well as your discount rates).

Put simply, the effects of expected inflation in valuation cancel out, and that is that the basis of what I would term “the currency invariance theorem”, where the value of a project or company should not change, if you change the currency in which you do your analysis. A project that has a positive NPV, when the analysis is done in US $, should continue to have the same positive NPV, if you redo the analysis in EGP, and a company that is overvalued, when the valuation is in US $, will remain overvalued, if you revalue it in EGP. The currency you chose to do an analysis is cannot alter the underlying value but that does not mean that changes in inflation cannot change the values of businesses, since that effect will depend on how well a company can pass inflation through to its customers (with pricing power), and I examined that relationship in 2022, after inflation had a resurgence in the United States after a decade of being low and boring. 

Thus, high inflation in Turkish lira has undoubtedly wreaked havoc the value of some Turkish companies, but given that damage, my point is that revaluing these companies in Euros will not undo that damage.

The Bottom Line

   As globalization gets a blowback, and in the midst of turmoil from tariffs, we got a reminder of how, much as we may want to go back to simpler times where the rest of the world did not intrude into our lives, we are all connected in good and bad ways. Thus, you may disagree with me on how to measure country risk and to bring into your analysis and investments, but it is undeniable that risk varies across countries and that we must incorporate that risk into our decision making. I hope that this post expose the layers in the process from the drivers of country risk to how these drivers play out as differences in country ratings, default spreads and equity risk premiums, while illustrating th how country risk can change over time, and sometimes in short periods.

YouTube Video

Datasets

1. Equity risk premiums by country at the start of 2026
2. Differential-inflation riskfree rates, by currency, at the start of 2026

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