Musings on Markets: January 2026

Wednesday, January 28, 2026

Data Update 3 for 2026: The Trust Deficit - Bonds, Currencies, Gold and Bitcoin!

In my last post, I talked about the disconnect between the bad news stories that we were reading and the solid performance of US equities during 2025. In this one, I want to focus specifically on four news stories from last year - the US announcement of punitive tariffs on the rest of the world, the downgrade of the US, the longest shutdown in US government history and unprecedented challenges to the Fed's perceived independence - and examine how they played out in the rest of the market. I will start with a look at US treasuries, which should have been in the eye of the storm in all of the stories, move on to to currencies, with a focus on the US dollar, then to gold & silver, and close off with a riff on bitcoin. As I look at these diverse markets, with very different outcomes in 2025, I will argue that a loss of trust in institutions (governments, central banks, regulatory authorities) was the thread that best explains their performance.

The Trust Narrative

We often underestimate how much of the global economy and financial markets are built on trust - in central banks to preserve the buying power in currencies, in governments and businesses to honor their contractual commitments, in legal systems to enforce them and in norms restraining behavior. That trust can be tenuous, and when violated, not only can the consequences can be catastrophic, but regaining lost trust can be a long, arduous process. In fact, one of the divides between developed and emerging markets for much of the last century was on the trust dimension, with the implicit assumption that emerging countries were less trustworthy than developed countries. That distinction has been muddied in the twenty first century, as crises and political developments have undercut trust in institutions across the board.

I would argue that 2025 was a particularly testing year, as developments in the United States, a dominant player in the global economy and markets, shook trust, and that loss of trust reverberated across its trading partners and global investors.

The first of the developments was on the tariff front, where decades of progress towards reducing barriers to trade and establishing predictability was upended on Liberation day (on March 31, 2025), where the US imposed what seemed like arbitrary tariffs on countries, but made those tariffs punitively large. In the immediate aftermath, equity markets around the world went into free fall, and I wrote a post in April 2025 about the tariff effect.
Just two weeks later, on April 16, 2025, Moody's, which had been the lone holdout among the ratings agencies in preserving a Aaa rating for the US, lowered its rating, albeit marginally to Aa1, reducing the number of Aaa rated countries in the world to eight. That rating, though not a complete surprise, still had shock value, and created ripple effects for appraisers and analysts, and I made my assessment in a post in May 2025.
On October 1, 2025, the US government went into shutdown mode, as congress balked at increasing the debt limit for the country and on the terms for a new budget, and unlike previous shutdowns, which lasted a few days, this one stretched into weeks, before an agreement was reached to reopen the government on November 12, 2025.
In the final months of the year, the independence of the Federal Reserve became a subject of discussion as news stories and pronouncements on social media suggested that the administration was seeking to put its imprint on monetary policy, through its nominees.

Depending on your political persuasion, you may have been one side of the debate or the other about each of these developments, but each of them chipped away at trust in the US government and its institutions.

While Donald Trump is the easy answer to why trust is slipping, the truth is that in each case, the slippage has been occurring over much longer. The push towards uninhibited global trade started running out of steam a decade or more ago, as the costs created political backlash. The Moody's ratings downgrade followed similar actions by S&P, in 2011, and Fitch, in 2023, partly in reaction to government deficit/borrowing and partly to political dysfunction. The Fed's much-vaunted independence has always been built more on norms rather that legal strictures, and administrations through the decades have managed to nudge central banks to adopt their preferred paths, and especially so in the aftermath of the pandemic.

The Bond Market

The effect of a loss of trust should be visible most clearly and immediately in the bond market, since bond buyers, of US treasuries, are doing so on the expectation that the US government will not default and that the Fed will do its utmost to preserve the dollar's buying power (and keep inflation low). Since the shocks from the news stories listed in the section above have the potential to alter both default risk and expected inflation, I looked at the movement of US treasuries over the course of 2025:

US Treasury Data

As you can see, there was little movement in 20-year and 30-year treasuries over the course of the year, but rates dropped, and neither the Moody's downgrade nor the government shutdown had much effect, and the rise in rates around the downgrade (in April) were more in response to tariffs and preceded the downgrade announcement. In fact, in the face of all of the bad news, the ten-year treasury rate dropped by 39 basis points (from 4.58% to 4.19%) during the year, and short term treasuries dropped even more, effectively altering the slope of the yield curve. To capture that effect, I looked at the evolution of the difference between rates across different maturities over the course of the year:

US Treasury Data

During 2025, the spread between the 10-year and 30-year treasury doubled, the spread between the 10-year and 2-year increased by seven basis points, but at the short end of the maturity spectrum, the spread between the two year and three month treasuries decreased. The net effect was a much more upward sloping yield curve at the end of 2025 than at its start, and while I do not attribute the power to to the yield curve as a prognosticator of future economy growth that some do, it is still marginally a positive sign for the US economy.

To gauge how the news stories played out on the perception of US government default, I looked at the sovereign CDS spreads for the US, market-set numbers capturing the cost of buying insurance against US government default, in 2025:

After a blip in April, where the sovereign CDS spreads increased from 0.4% to just over 0.5% in April 2025, spreads have dropped back to levels lower than they were at the start of the year.

To get a sense of how expectation of inflation changed over the course of the year, I turned again to a market-based number from the treasury market, where the difference between the US ten-year treasury bond rate and the ten-year US treasury TIPs rate (a real rate) operates as a measure of expected inflation:

In 2025, these estimates suggest that the expected inflation barely budged, ending the year lower than it was at the start. That would have put the market at odds with experts, who forecasted a surge in inflation especially after the tariffs were announced, but would have put it in sync with actual inflation reported during the rest of the year.

On the final question of why the Fed independence fight has not created more turmoil in markets, I start with a different perspective from most, since I believe that the role of Fed in setting interest rates is vastly overstated. As I note in that post, the Fed's much publicized forays into changing the Fed Funds rate has some effect on the short term treasuries, but long term treasuries are driven less by the Fed’s actions (or inaction) and more by expected inflation and real growth. I capture that relationship every year by estimating an intrinsic ten-year riskfree rate, obtained by summing together actual inflation for the year and real GDP growth and comparing it to the ten-year treasury bond rate:

Download intrinsic riskfree data

Over the seventy years of data in this graph, it is clear that the big movements in treasury rates are captured in the intrinsic risk free rate, with higher inflation in the 1970s coinciding with the rise in the treasury rate, and the sustained low rates of the last decade largely in sync with the low inflation and anemic growth during the period. As you can see , after a stint (2021-25) where the intrinsic risk free rate was well above the ten-year treasury rate, largely because of higher inflation, the treasury rate of 4.18%, at the start of 2026, is within reach of the intrinsic rate of 5.10%, obtained by adding inflation and real growth in 2025. That said, though, I do think that the reason that treasury rates stayed well below the intrinsic risk free rate during this period is because markets believed that the Fed would use its powers to try to get inflation under control, even at the expense of a slowing economy (or a recession). It is this belief that will be put at risk if the Fed becomes viewed as an extension of the government, increasing the risks of inflation spiraling out of control, creating a cycle where higher inflation causes higher interest rates, and attempts by central banks to lower these rates actually feed into even higher inflation. It is in the best interests of governments and politicians to let central banks be independent and set rates, because it will lead to better economic outcomes and lower interest rates, while giving politicians cover for unpleasant choices that have to be made to deliver these results.

I complete the assessment of the bond market in 2025 by looking at corporate bonds, and especially at the default spreads of corporate bonds in different ratings classes during the course of the year:

There seems to be a divergence in how the year played out in the corporate bond market, with the higher rated bonds all seeing flat or lower spreads, but bonds below investment grade (below BBB) seeing an increase in spreads.

The Currency Market

Just as bond markets are driven by trust that governments will not default, unless it has run out of options, and that central banks will protect a currency’s buying power, currency markets are swayed by the same concerns. Here, a split emerged between the bond and currency markets. While bond markets, for the most part, took the news stories of the year in stride, the dollar was clearly knocked off balance, and it weakened over the course of the year, as can be seen in the graph below;

The trade-weighted dollar, a broad index of the dollar against multiple currencies, was down 7.24% for the year, but the dollar lost more value against developed market currencies than against emerging market currencies; it was down 8.19% against the former and 6.34% against the latter.

Gold and Silver

When investors lose trust in governments and central banks, it should come as not surprise that their money leaves financial asset markets and goes into collectibles, and in a post in October 2025, I looked at how this played out specifically in the gold market. In 2025, Gold had one of its best years ever, rising 65% during the year, and silver, the other widely held precious metal, had an even bigger year, rising 148% during the year:

The surge in precious metal prices in 2025 was unusual, at least on one dimension. Gold and silver prices tend to rise during periods of unexpectedly high inflation (1970s) or during intense crises, but at least in 2025, neither seemed to be at play. As we noted earlier, inflation came in much tamer than expected, and equity and equity and bond markets, after a brief meltdown in April, showed no signs of trauma. In fact, if you scale gold price to the CPI, the basis for the golden rule, where the argument that gold rises at roughly the inflation rate over time, gold price performance in 2025 broke the indicator, as the ratio of gold price to the CPI exploded well above historic norms.

It is worth noting that a loss of trust in the US government and, by extension, in the US dollar, have translated into increases in gold holdings at central banks, but that increase, while contributing to gold's allure, cannot explain its price rise during the year. If the rise in gold prices was a surprise, the rise in silver prices was even more so, and in 2025, silver prices rose enough to bring the ratio of gold to silver prices to below the long term median value:

It seems like the market is pulling in different directions on the trust question, with stocks and bonds largely underplaying them, the currency markets indicating some worry and gold and silver suggesting much bigger consequence to the loss of trust. That does not surprise me since the market is not a monolith, and while the broad investor base might have adopted the response of "What, me worry?", there is a significant segment of investors that see catastrophic risks emerging, and piling into precious metals.

Bitcoin

I have written off and on about bitcoin over the last fifteen years, and have generally straddled the middle, with both sides of the divide (bitcoin optimists and bitcoin doomsayers) taking issue with me. I have argued that bitcoin can be viewed either as a a central-bank free currency, designed by the paranoid for the paranoid, or millennial gold (a collectible), and that we would know better as we saw how it performed in response to macro developments. In many ways, 2025 provided us with a test, which should, if nothing else, advance our understanding of the endgame for bitcoin. In a year where the dollar was weakened as a global currency and central banking independence was questions, you would have expected to see bitcoin do well, both because of its status as a currency without a central bank and as a collectible. The actual price path for bitcoin, in US dollars and Euros, is captured below:

After setbacks in the first third of the year, bitcoin's price surged upwards in the middle of the year, making those who had built their narratives around it to look good. In my post on bitcoin on July, I focused on the suggestion that other companies should follow the Microstrategy path and put their cash balances into bitcoin, and argued that it was not a good idea. The months following have vindicated that view, as both bitcoin and Microstrategy have seen pricing collapses, and bitcoin ended the year down 6.4% in US dollar terms and 17.4% in Euro terms.

It remains too early in bitcoin's life to pass final judgment, but if the story for bitcoin is that it will draw in investors who have lost trust in governments and central banks, it is clear that gold and silver were the draws, at least in 2025, not bitcoin. As a final assessment of how the different asset classes moved in relation to each other, I looked at weekly returns in 2025 in six markets - bitcoin, gold, silver, large US stocks, small US stocks and the ten-year treasury bond - and computed correlations across the assets:

There are only a few co-movements which are large enough to be statistically significant. The first is that bitcoin is much more highly correlated with US equities than it is with its collectible counterparts, suggesting that it draws in risk seekers, not the risk averse. The second is that notwithstanding the fact that US treasuries did very little over the course of the year, on a week-to-week basis, their movements affected stock prices. At least in 2025, higher interest rates (translating into negative bond returns) were accompanied by higher stock prices, casting doubt on the notion that the stock market is being held afloat by Fed activity or inactivity.

Conclusion

The big news stories of the year, from the ratings downgrade to the government shutdown to the soap opera of who would lead the Fed all fed into a storyline of fraying trust in US institutions. While that trust deficit should have led to rising interest rates and a tough year for bonds, actual bond market performance, like equities in the prior post, suggested that markets were not swayed. That clearly does not mean that no one cared, since a subset of investors were concerned enough about the trust issue to push the dollar down and put gold and silver prices on stratospheric upward paths. Bitcoin remained the outlier, moving more with stocks and bonds, albeit without their upside (at least this year) and less with collectibles.

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Friday, January 23, 2026

Data Update 2 for 2026: Equities get tested, and pass again!

It was a disquieting year , as political and economic news stories shook the foundations of the post-war economic order, built around global trade and the US dollar. In fact, if you had been read just the news all through the year, and were shielded from financial markets, and been asked what stocks did during the year, you would have guessed, based on the news, that they had a bad year. You would have been wrong, though, as equity markets proved resilient (yet again) and delivered another solid year of returns for investors. In this post, I will focus on US equities, starting with the indices, and then deconstructing the data to see the differences in the cross section. As has been my practice for the last few years, I will also use this post to update the equity risk premium for the S&P 500, my composite indicator for whether the market is richly priced or not, and estimate a value for the index, with a "reasonable" equity risk premium.

Back from the Brink: US Equities in 2025

At the start of 2025, the consensus view was that stocks were primed to do well, helped by what investors perceived would be a business-friendly administration and a Federal Reserve, ready to cut rates. In keeping with Robert Burn's phrase that the best-laid plans of mice and men go awry, the year did not measure up to those expectations at least in terms of policy and rate changes, but stocks still managed to find a way through. Let's start with a look at the S&P 500 and the NASDAQ, day-to-day through the year:

The first few weeks of 2025 saw of continuation of the momentum built up after the 2024 elections and stock prices continued upwards, but February and March saw a drawdown in stock prices as talk of tariffs and trade wars heated up before culminating in a dramatic sell off in early April, after liberation day, when breadth and magnitude of the tariffs blindsided markets. The sell off was brutal and short, and stocks hit their low point for the year on April 11, 2025. Over the next few months, stocks mounted a comeback, before leveling off at the end of September and coasting for the rest of the year. Early in the year, the S&P 500 held its value better than the NASDAQ, generating talk of a long-awaited tech sell off, but as stocks recovered in the subsequent months, the NASDAQ ended up moving ahead the S&P 500.

Across the entire year, the S&P 500 rose from 5881.6 to 6845.5, delivering price appreciation of 16.39% for the year. The dividends on the companies in the index for the year, based upon dividends in the first three quarters of 2025 and estimates for dividends in the last quarter amounted added a yield of 1.34%.

The S&P 500's return in 2025 of 17.72% was a solid year, but to provide perspective on how it measures up to history, I looked at annual returns from US stocks from 1928 to 2025, and computed distributional statistics:

Download data

While 2025's annual returns put it in the right in the middle of the distribution, close to the median and ranked 45th of the 98 years of US equity returns from 1928-2026, it represented a third consecutive year when the annual stock return exceeded the median returns, the longest streak since the mid 1990s; US equities between 2023 and 2025, a period where many market timers were suggesting not just caution but staying out the market, returned 85.32% to investors.

Deconstructing US Stock Price Performance

While stocks had a good year overall, the spoils were dividend unequally, as if often the case, across industries and sectors. To take a closer look at where the best and worst performance was in 2025, I started by looking at a breakdown by sector, where I computed the returns based on the change in aggregate market capitalization in 2025:

I have tracked the performance of each sector, by quarter, and across the year a measured the returns. The best performing sector in percentage returns was communication services (which includes Alphabet and Meta), up 30.63% for the year, followed by technology, which continued it sustained run of success by delivering 23.65% as an annual return; on a dollar value basis, it was not close with technology companies posting an increase of $4.17 trillion in market cap during the year. The worst performing sectors were consumer staples and real estate where the returns were about 2% for the year.

The problem with sector categorizations is the they are overly broad and include very diverse industry groupings, and to overcome that problem, I looked at returns by industry, with a breakdown into 95 industry groups. While you can find the full list at the end of this post, I ranked the industry returns in 2025, from best to worst, and extract the ten best and worst performing industry groups:

Download industry returns in 2025

The surge in gold and silver prices in 2025 carried precious metals companies to the top of the list, with a return of 169.2% for the year, and other energy and mining companies also made the best performer list, with a scattering of technology standouts. The worst performing businesses were primarily old economy, with chemicals, consumer product companies and food processing all struggling during the year.

One of the major changes that we have seen in cross sectional differences in the twenty first century has been the fading or even disappearance of two well documented phenomena from the twentieth century, the first being the small cap premium, where small market cap companies delivered much higher risk-adjusted returns that large market cap companies, and the value premium, where low price to book stocks beat high price to book stocks in the return game. I focused in how these categorizations behaved in 2025, and we did see small cap stocks and low price to book stocks return, at least in part, to favor:

If you are small cap or a value investor, though, I would not be celebrating the return on these premia, but I do think that we will start to see a return to balance, where the groupings will trade off winning in some years for losing in others.

As a final assessment, I did look at the seven stocks that have not only carried the market for the last few years, the Mag Seven, but have been the source of much hand wringing about how markets are becoming top-heavy and concentrated. I started by looking at the individual companies, and how they performed in 2025:

While the Mag Seven saw their collective market capitalization increase by 22.36%, Apple and Amazon lagged with single digit increases, and Nvidia (up 37.8%) and Alphabet (up 62.7%) for the year. Increasingly, the Mag Seven are diverging in their price paths, and that should be expected since they operate in very different businesses and have very different management running them. To examine how much the Mag Seven have carried the market, I tracked the market cap of the Mag Seven against the rest of US equity (close to 6000 companies) from 2014 through the four quarters of 2025.

The aggregate market cap of the Mag Seven has increased from 11% of the US equity market (composed of close to 6000 stocks) in 2014 to 30.89% of the market at the end of 2025, with the $3.9 billion in market cap added in 2025 accounting for 39.3% of the overall increase in market capitalization of all US equities during the year. While this Mag Seven party will undoubtedly end at some point, it did not happen in 2025.

US Equities: Too high, too low or just right?

This post, at least so far, has been a post mortem of the year that was, but investing is always about the future, and the question that we all face as investors, is where stocks will go this year. In my unscientific assessment of stock market opinion, from experts and market timers, there seems to a decided tilt towards bearishness at the start of 2026, for a variety of reasons. There are some who note that having had three good years in a run, stocks will take breather. Others point to history and note that stocks generally don't do well in the second years of presidential terms. The most common metric that bearish investors point to, though, is the PE ratio for stocks at the start of 2026 is pushing towards historic highs, as can be seen in the graph below, where I look at three variants on the PE ratio - a trailing PE, where I divide the index by earnings in the most recent 12 months, a normalized PE, where I divide the index by the average earnings over the last ten years and a Shiller PE, where I average inflation-adjusted earnings over the last ten years:

Download historical PE ratios for US equities

Using every PE ratio measure, it is undeniable that the PE ratio for the S&P 500, at the start of 2026, is much higher than it has been at any extended period in history, perhaps with the exception with the late 1990s. While this may sound like a slam dunk argument for US stocks being over priced, it is worth remembering that this indicator would have suggested staying out of US equities for much of the last decade. The problem with the PE pricing metric is that it is noisy and an unreliable indicator, and before you use it to build a case that equity investors in the US have become irrational, you may want to consider reasons why US stocks have benefited able to fight the gravitational forces of mean reversion.

1. Robust Earnings Growth & Earnings Resilience: In this century, US stocks have increased more than four-fold, with the S&P 500 rising from 1320.28 at the end of 2000 to 6845.5 at the end of 2025, but it is also worth noting that US companies have also had a solid run in earnings, with earnings increasing about 356% during that same time period.

It is also notable that not only did earnings register strong growth over this period, there were only three years in this century when earnings declined - 2001 (dot com bust), 2009 (2008 crisis) and 2020 (Covid). US companies have become more resilient in terms of delivering earnings through recessions and other crises, pointing to perhaps less risk in equities. I will return in a later post to examine why that may be, with some of the answers rooted in changes in US equity market composition and some in management behavior.

2. Healthy cash returns: In conjunction with delivering earnings growth, US companies have also been returning large amounts of cash to their shareholders, albeit more in buybacks than in conventional dividends. In 2025, the companies in the S&P 500 alone returned more than a trillion dollars in cash flows in buybacks, and in the graph below, I look at how the augmented cash yield (composed of dividends and buybacks) has largely sustained the market:

While the dividend payout ratio, computed using only dividends, has been on a downward trend all through this century, adding buyback to dividends and computing a cash yield ratios yields values that are comparable to what dividend yields used to be, before the buyback era.

In sum, you can see why both bulls and bears retreat to their favored arguments, and there is no obvious tie breaker. The level of stock prices (PE ratios) should be a concern, but you cannot dismiss the benefits of growing and resilient earnings, and substantial cash return. To break the tie, in a very self serving away, I will revert to my favored metric for the US equity market, the implied equity risk premium, which in addition to looking at stock price levels, the growth in earnings and the cash return, also brings in the level of rates. The implied equity risk premium, as I compute it, is the based upon the index level and the expected cashflows (from dividends and buybacks, augmented by earnings growth), and very simply, is an internal rate of return for stocks. Netting out the riskfree rate yields an equity risk premium. The table below contains the computation of the implied ERP at the start of 2026:

Download spreadsheet

Given the index level on January 1, 2026, of 6845.5, and the expected cash flows that I computed on that date (using the dividends and buybacks in the trailing 12 months as my starting point, and growing them at the same rate as earnings), I obtain an expected return on stocks of 8.41%. Subtracting out the US T. Bond rate (dollar riskfree rate) of 4.18% (3.95%) on that day yields an equity risk premium of 4.23% (4.46%) for the US. I want to emphasize again that this estimate is entirely a market-driven number and is model-agnostic.

If you are wondering how estimating this numbers lets you make a judgment on whether US stocks are over priced, all you need to reframe the equity risk premium by asking whether the current ERP is, in your view, too high, too low or just right.

If you believe that the market is pricing in too low an ERP, given the risks that are on the horizon, you are contending the stocks are over priced.
If your view is that the current ERP is too high, that is equivalent to arguing that stocks today are under priced.
If you are not a market timer, you are in effect arguing that the current ERP is, in fact, the right ERP for the market.

To illustrate this point, I have estimated the value of the index at equity risk premiums ranging from 2% to 6%:

With a 2% equity risk premium, you get an astounding value of 14834 for the S&P 500, which would make the index undervalued by 53%. At the other end of the spectrum, with a 6% equity risk premium, the index should trade at 4790, translating into an overvaluation of 43%. So, is the ERP of 4.23% (I will revert to this number, since my historical numbers did use the US treasury bond rate as the riskfree rate) at the start of 2026 a high, low or just-right number? Rather than make that judgment for you, I have computed the implied ERP for the S&P 500 going back to 1960:

Download historical implied ERP

There is something in this graph that almost every investor group can take comfort in, If you are market neutral, you will take comfort from the fact that the current ERP is almost exactly equal to the average for the 1960-2025 period. If you are bearish you will point to the fact that the ERP now is lower than it has been in the post-2008 period, backing up your case that an adjustment is overdue. I am leery of the bubble word, especially used in the context of this market, since unlike the end of 1999, when the ERP got as low as 2.05%, the current ERP is more in the middle of the historic range.

The Bottom Line

US equities had a good year in 2025, and there are signs of excess in at some parts of the market, especially related to AI. That said, the capacity of US companies to continue to deliver earnings and return cash flows even in the face of a tsunami of bad news continues to sustain the market. I am, at my core, a non market-timer, but I have held back on putting idle cash back into US equities in the last year, preferring to keep that cash in treasury bills. It is entirely possible that the market will continue to prove the naysayers wrong and post another strong year, but much as it may pain equity investors, the healthiest development for the market would be for it to deliver a return roughly equal to its expected return (8-9%) and clean up on pricing overreach along the way. For the bears, this may also be the year when the bad news stories of last year, including tariffs and political whiplash, will finally start to hit the bottom line, reducing aggregate earnings and cash flows, but waiting on the sidelines for this to happen has not been a good strategy for the last decade.

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Implied ERP computation for the S&P 500 on January 1, 2026

Friday, January 9, 2026

Data Update 1 for 2026: The Push and Pull of Data!

In my musings on valuation, I have long described myself as more of a number cruncher than a storyteller, but it is because I love numbers for their own sake, rather than a fondness for abstract mathematics. It is that love for numbers that has led me at the beginning of each year since the 1990s to take publicly available data on individual companies, both from their financial statements and from the markets that they are listed and traded on, and try to make sense of that data for a variety of reasons - to gain perspective, to use in my corporate financial analysis and valuations and to separate information from disinformation . As my access to data has improved, what started as a handful of datasets in my first data update in 1994 has expanded to cover a much wider array of statistics than I had initially envisioned, and my 2026 data updates are now ready. If you are interested in what they contain, please read on.

The Push and Pull of Data

After a year during which we heard more talk about data and data centers than ever before in history, usually in the context of how AI will change our lives, it is worth considering the draw that data has aways had on not just businesses but on individuals, as well as the dangers with the proliferation of data and the trust we put on that data.

In a world where we feel adrift and uncertain, the appeal of data is clear. It gives us a sense of control, even if it is only in passing, and provides us with mechanisms for making decisions in the face of uncertainty.

Signal in the noise: Anyone who has to price/value a stock or assess a project at a firm has to make estimates in the face of contradictions, both in viewpoints and in numbers. The entire point of good data analysis is to find the signals in the noise, allowing for reasoned judgments, albeit with the recognition that you will make mistakes.
Coping mechanism for uncertainty: Investors and businesses, when faced with uncertainty, often respond in unhealthy ways, with denial and paralysis as common responses. Here again, data can help in two ways, first by helping you picture the range of possible outcomes and second by bringing in tools (simulations, data visualizations) for incorporating uncertainty into your decision-making.
Prescription against tunnel vision: It is easy to get bogged down in details, when faced with having to make investment decisions, and lose perspective. One of the advantages of looking at data differences over time and across firms is that it can help you elevate and regain perspective, separating the stuff that matters a lot from that which matters little.
Shield from disinformation: At the risk of getting backlash, I find that people make up stuff and present it as fact. While it is easy to blame social media, which has provided a megaphone for these fabulists, I read and hear statements in the media, ostensibly from experts, politicians and regulators, that cause me to do double takes since they are not just wrong, but easily provable as wrong, with the data.

While data clearly has benefits, as a data-user, I do know that it comes with costs and consequences, and it behooves us all to be aware of them.

False precision: It is undeniable that attaching a number to something that worries you, whether it be your health or your finances, can provide a sense of comfort, but there is the danger with treating estimates as facts. In one of my upcoming posts, for instance, I will look at the historical equity risk premium, measured by looking at what stocks have earned, on an annual basis, over treasury bonds for the last century. The estimate that I will provide is 7.03% (the average over the entire period), but that number comes with a standard error of 2.05%, resulting in a range from a little less than 4% (7.03% - 2 × 2.05%) to greater than 11%. This estimation error plays out over and over again in almost every number that we use in corporate finance and valuation, and while there is little that can be done about it, its presence should animate how we use the data.
The Role of Bias: I have long argued that we are all biased, albeit in varying degrees and in different directions, and that bias will find its way into the choices we make. With data, this can play out consciously, where we use data estimates that feed into our biases and avoid estimates that work in the opposite direction, but more dangerously, they can also play out subconsciously, in the choices we make. While it is true that practitioners are more exposed to bias, because their rewards and compensation are often tied to the output of their research, the notion that academics are somehow objective because their work is peer-reviewed is laughable, since their incentive systems create their own biases.
Lazy mean reversion: In a series of posts that I wrote about value investing, at least as practiced by many of its old-time practitioners, I argued that it was built around mean reversion, the assumption that the world (and markets) will revert back to historic norms. Thus, you buy low PBV stocks, assuming (and hoping) that those PBV ratios will revert to market averages, and argue that the market is overpriced because the PE ratio today is much higher than it has been historically. That strategy is attractive to those who use it, because mean reversion works much of the time, but it is breaks down when markets go through structural shifts that cause permanent departures from the past.
The data did it: As we put data on a pedestal, treating the numbers from emerge from it as the truth, there is also the danger that some analysts who use it view themselves as purely data engineers. While they make recommendations based upon the data, they also refuse to take ownership for their own prescriptions, arguing that it is the data that is responsible.

As the data that we collect and have access to gets richer and deeper, and the tools that we have to analyze that data become more powerful, there are some who see a utopian world where this data access and analysis leads to better decisions and policy as output. Having watched this data revolution play out in investing and markets, I am not so sure, at least in the investing space. Many analysts now complain that they have too much data, not too little, and struggle with data overload. At the same time, a version of Gresham's law seems to be kicking in, where bad data (or misinformation) often drives out good data, leading to worse decisions and policy choices. My advice, gingerly offered, is that as you access data, it is caveat emptor, and that you should do the following with any data (including my own):

(a) Consider the biases and priors of the data provider.

(b) Not use data that comes from black boxes, where providers refuse to detail how they arrived at numbers.

Data Coverage

As I mentioned at the start of this post, I started my data estimation for purely selfish reasons, which is that I needed those estimates for my corporate financial analyses and valuations. While my sharing of the data may seem altruistic, the truth is that there is little that is proprietary or special about my data analysis, and almost anyone with the time and access to data can do the same.

Data Sources

At the risk of stating the obvious, you cannot do data analysis without having access to raw data. In 1993, when I did my first estimates, I subscribed to Value Line and bought their company-specific data, which about 2000 US companies and included a subset of items on financial statements, on a compact disc. I used Value Line's industry categorizations to compute industry averages on a few dozen items, and presented them in a few datasets, which I shared with my students. In 2025, my access to data has widened, especially because my NYU affiliation gives me access S&P Capital IQ and a Bloomberg terminal, which I supplement with subscriptions (mostly free) to online data. It is worth noting that these almost all the data from these providers is in the public domain, either in the form of company filings for disclosure or in government macroeconomic data, and the primary benefit (and it is a big one) is easy access.

As my data access has improved, I have added variables to my datasets, but the data items that I report reflect my corporate finance and valuation needs. The figure below provides a partial listing of some of these variables:

As you can see from browsing this list, much of the data that I report is at the micro level, and the only macro data that I report is on variables that I need in valuation, such as default spreads and equity risk premiums. In computing these variables, I have tried to stay consistent with my own thinking and teaching and transparent about my usage. As an illustration for consistency, I have argued for three decades that lease commitments should be treated as debt and that R&D expenditures are capital, not operating, expenses, and my calculations have always reflected those views, even if they were at odds with the accounting rules. In 2019, the accounting rules caught up with my views on lease debt, and while the numbers that I report on debt ratios and invested capital are now closer to the accounting numbers, I continue to do my own computations of lease debt and report on divergences with accounting estimates. With R&D, I remain at odds with accountants, and I report on the affected numbers (like margins and accounting return) with and without my adjustments. On the transparency front, you can find the details of how I computed each variable at this link, and it is entirely possible that you may not agree with my computation, it is in the open.

There are a few final computational details that are worth emphasizing, and especially so if you plan to use this data in your analyses:

With the micro data, I report on industry values rather than on individual companies, for two reasons. The first is that my raw data providers are understandably protective of their company-level data and have a dim view of my entry into that space. The second is that if you want company-level data for an individual company or even a subset, that data is, for the most part, already available in the financial filings of the company. Put simply, you don't need Capital IQ or Bloomberg to get to the annual reports of an individual company.
For global statistics, where companies in different countries are included within each industry, and report their financials in different currencies, I download the data converted into US dollars. Thus, numbers that are in absolute value (like total market capitalization) are in US dollars, but most of the statistics that I report are ratios or fractions, where currency is not an issue, at least for measurement. Thus, the PE ratio that I report would be the same for any company in my sample, whether I compute it in US dollar or Chilean pesos, and the same can be said about accounting ratios (margins, accounting returns).
While computing industry averages may seem like a trivial computational challenge, there are two problems you face in large datasets of diverse companies. The first is that there will be individual companies where the data is missing or not available, as is the case with PE ratios for companies with negative earnings. The second is that the companies within a group can vary in size with very small and large companies in the mix. Consequently, a simple average will be a flawed measure for an industry statistic, since it weighs the very small and the very large companies equally, and while a size-weighted average may seem like a fix, the companies with missing data will remain a problem. My solution, and you may not like it, it to compute aggregated values of variable, and use these aggregated values to compute the representative statistics. Thus, my estimate the PE ratio for an industry grouping is obtained by dividing the total market capitalization of all companies in the grouping by the total net income of all companies (including money losers) in the grouping.

Since my data is now global, I also report on these variables not only across all companies globally in each industry group, but for regional sub-groupings:

I will admit that this breakdown may look quirky, but it reflects the history of my data updates. The reason Japan gets its own grouping is because when I started my data grouping two decades ago, it was a much larger part of both the global economy and markets. The emerging markets grouping has become larger and more unwieldy over time, as some of the countries in this group had or have acquired developed market status and as China and India have grown as economies and markets, I have started reporting statistics for them separately, in addition to including them in the emerging markets grouping. Europe, as a region, has become more dispersed in its risk characteristics, with parts of Southern Europe showing the volatility more typical of emerging markets.

Data Universe

In the first part of this post, I noted how bias can skew data analysis, and one of the biggest sources of bias is sampling, where you pick a subset of companies and draw the wrong conclusions about companies. Thus, using only the companies in the S&P 500 or companies that market capitalizations that exceed a billion in your sample in computing industry averages will yield results that reflect what large companies are doing or are priced at, and not the entire market. To reduce this sampling bias, I include all publicly traded companies that have a market price that exceeds zero in my sample, yielding a total sample size of 48,156 companies in my data universe. Note that there will be some sampling bias still left insofar as unlisted and privately owned businesses are not included, but since disclosure requirements for these businesses are much spottier, it is unlikely that we will have datasets that include these ignored companies in the sample in the near future.

In terms of geography, the companies in my sample span the globe, and I will add to my earlier note on regional breakdowns, by looking at the number of firms listed and market capitalizations of companies in each sub-region:

Current data link

As you can see, the United States, with 5994 firms and a total market capitalization of $69.8 trillion, continues to have a dominant share of the global market. While US stocks had a good year, up almost 16.8% in the aggregate, the US share of the global market dipped slightly from the 48.7% at the end of 2024 to 46.8% at the end of 2025. The best performing sub-region in 2025 was China, up almost 32.5% in US dollar terms, and the worst, again in US dollar terms, was India, up only 3.31%. Global equities added $26.3 trillion in market capitalization in 2025, up 21.46% for the year.

While I do report averages by industry group, for 95 industry groupings, these are part of broader sectors, and in the table below, you can see the breakdown of the overall sample by sector:

Current data link

Across all global companies, technology is now the largest share of the market, commanding almost 22% of overall market capitalization, followed by financial services with 17.51% and industrials with 12.76%. There is wide divergence across sectors, in terms of market performance in 2025, with technology delivering the highest (20.73%) and real estate and utilities the lowest. There is clearly much more that can be on both the regional and sector analyses that can enrich this analysis, but that will have to wait until the next posts

Usage

My data is open access and freely available, and it is not my place to tell you how to use it. That said, it behooves me to talk about both the users that this data is directed at, as well as the uses that it is best suited for.

For practitioners, not academic researchers: The data that I report is for practitioners in corporate finance, investing and valuation, rather than academic researchers. Thus, all of the data is on the current data link is data as of the start of January 2026, and can be used in assessments and analysis today. If you are doctoral student or researcher, you will be better served going to the raw data or having access to a full data service, but if you lack that access, and want to download and use my industry averages over time, you can use the archived data that I have, with the caveat being that not all data items have long histories and my raw data sources have changed over time.
Starting point, not ending point: If you do decide to use any of my data, please do recognize that it is the starting point for your analysis, not a magic bullet. Thus, if you are pricing a steel company in Thailand, you can start with the EV/EBITDA multiple that I report for emerging market steel companies, but you should adjust that multiple for the characteristics of the company being analyzed.
Take ownership: If you do use my data, whether it be on equity risk premiums or pricing ratios, please try to understand how I compute these numbers (from my classes or writing) and take ownership of the resulting analysis.

If you use my data, and acknowledge me as a source, I thank you, but you do not need to explicitly ask me for permission. The data is in the public domain to be used, not for show, and I am glad that you were able to find a use for it.

The Damodaran Bot!

In 2024, I talked about the Damodaran Bot, an AI entity that had read or watched everything that I have put online (classes, books, writing, spreadsheets) and talked about what I could do to stay ahead of its reach. I argued that AI bots will not only match, but be better than I am, at mechanical and rule-based tasks, and that my best pathways to creating a differential advantage was in finding aspects of my work that required multi-disciplinary (numbers plus narrative) and generalist thinking, with intuition and imagination playing a key role. As I looked at the process that I went through to put my datasets together, I realized that there was no aspect of it that a bot cannot do better and faster than I can, and I plan to work on involving my bot more in my data update next year, with the end game of having it take over almost the entire process.

I do think that there is a message here for businesses that are built around collecting and processing data, and charging high prices for that service. Unless they can find other differentials, they are exposed to disruption, with AI doing much of what they do. More generally, to the extent that a great deal of quant investing has been built around smart numbers people working with large datasets to eke out excess returns, it will become more challenging, not less so, with AI in the mix.

YouTube Video

Links to data