Data Update 4 for 2025: Interest Rates, Inflation and Central Banks!

It was an interesting year for interest rates in the United States, one in which we got more evidence on the limited power that central banks have to alter the trajectory of market interest rates. We started 2024 with the consensus wisdom that rates would drop during the year, driven by expectations of rate cuts from the Fed. The Fed did keep its end of the bargain, cutting the Fed Funds rate three times during the course of 2024, but the bond markets did not stick with the script, and market interest rates rose during the course of the year. In this post, I will begin by looking at movements in treasury rates, across maturities, during 2024, and the resultant shifts in yield curves. I will follow up by examining changes in corporate bond rates, across the default ratings spectrum, trying to get a measure of how the price of risk in bond markets changed during 2024.

Treasury Rates in 2024

    Coming into 2024, interest rates had taken a rollicking ride, surging in 2022, as inflation made its come back, before settling in 2023. At the start of 2024, the ten-year treasury rate stood at 3.88%, unchanged from its level a year prior, but the 3-month treasury bill rate had climbed to 5.40%. In the chart below, we look the movement of treasury rates (across maturities) during the course of 2024:

Download daily data

During the course of 2024, long term treasury rates climbed in the first half of the year, and dropped in the third quarter, before reversing course and increasing in the fourth quarter, with the 10-year rate ending  the year at 4.58%, 0.70% higher than at the start of the year. The 3-month treasury barely budged in the first half of 2024, declined in the third quarter, and diverged from long term rates and continued its decline in the last quarter, to end the year at 4.37%, down 1.03% from the start of the year. I have highlighted the three Fed rate actions, all cuts to the Fed Funds rate, on the chart, and while I will come back to this later in this post, market rates rose after all three.

    The divergence between short term and long term rates played out in the yield curve, which started 2024, with a downward slope, but flattened out over the course of the year:

Download daily data

Writing last year about the yield curve, which was then downward sloping, I argued that notwithstanding prognostications of doom,  it was a poor prediction of recessions. This year, my caution would be to not read too much, at least in terms of forecasted economic growth, into the flattening or even mildly upward sloping yield curve. 

    The increase in long term  treasury rates during the course of the year was bad news for treasury bond investors, and the increase in the 10-year treasury bond rate during the course of the year translated into an annual return of -1.64% for 2024:

With the inflation of 2.75% in 2024 factored in, the real return on the 10-year bond is -4.27%. With the 20-year and 30-year bonds, the losses become larger, as time value works its magic. It is one reason that I argue that any discussion of riskfree rates that does not mention a time horizon is devoid of a key element. Even assuming away default risk, a ten-year treasury is not risk free, with a one time horizon, and a 3-month treasury is definitely not riskfree, if you have a 10-year time horizon.

The Drivers of Interest Rates

    Over the last two decades, for better or worse, we (as investors, consumers and even economics) seem to have come to accept as a truism the notion that central banks set interest rates. Thus, the answer to questions about past interest rate movements (the low rates between 2008 and 2021, the spike in rates in 2022) as well as to where interest rates will go in the future has been to look to central banking smoke signals and guidance. In this section, I will argue that the interest rates ultimately are driven by macro fundamentals, and that the power of central banks comes from preferential access to data about these fundamentals, their capacity to alter those fundamentals (in good and bad ways) and the credibility that they have to stay the course.

Inflation, Real Growth and Intrinsic Riskfree Rates

    It is worth noting at the outset that interest rates on borrowing pre-date central banks (the Fed came into being in 1913, whereas bond markets trace their history back to the 1600s), and that lenders and borrowers set rates based upon fundamentals that relate specifically to what the former need to earn to cover  expected inflation and default risk, while earning a rate of return for deferring current consumption (a real interest rate). If you set the abstractions aside, and remove default risk from consideration (because the borrower is default-free), a riskfree interest rate in nominal terms can be viewed, in its simplified form, as the sum of the expected inflation rate and an expected real interest rate:

Nominal interest rate = Expected inflation + Expected real interest rate

This equation, titled the Fisher Equation, is often part of an introductory economics class, and is often quickly forgotten as you get introduced to more complex (and seemingly powerful) monetary economics lessons. That is a pity, since so much of misunderstanding of interest rates stems from forgetting this equation. I use this equation to derive what I call an "intrinsic riskfree rate", with two simplifying assumptions:

1. Expected inflation: I use the current year's inflation rate as a proxy for expected inflation. Clearly, this is simplistic, since you can have unusual events during a year that cause inflation in that year to spike. (In an alternate calculation, I use an average inflation rate over the last ten years as the expected inflation rate.)
2. Expected real interest rate: In the last two decades, we have been able to observe a real interest rate, at least in the US, using inflation-protected treasury bonds(TIPs). Since I am trying to estimate an intrinsic real interest rate, I use the growth rate in real GDP as my proxy for the real interest rate. That is clearly a stretch when it comes to year-to-year movements, but in the long term, the two should converge.

With those simplistic proxies in place, my intrinsic riskfree rate can be computed as follows:

Intrinsic riskfree rate = Inflation rate in period t + Real GDP growth rate in period t

In the chart below, I compare my estimates of the intrinsic riskfree rate to the observed ten-year treasury bond rate each year:

Download data

While the match is not perfect, the link between the two is undeniable, and the intrinsic riskfree rate calculations yield results that help counter the stories about how it is the Fed that kept rates low between 2008 and 2021, and caused them to spike in 2022. 

• While it is true that the Fed became more active (in terms of bond buying, in their quantitative easing phase) in the bond market in the last decade, the low treasury rates between 2009 and 2020 were driven primarily by low inflation and anemic real growth. Put simply, with or without the Fed, rates would have been low during the period.
• In 2022, the rise in rates was almost entirely driven by rising inflation expectations, with the Fed racing to keep up with that market sentiment. In fact, since 2022, it is the market that seems to be leading the Fed, not the other way around.
  

Entering 2025, the gap between intrinsic and treasury rates has narrowed, as the market consensus settles in on expectations that inflation will stay about the Fed-targeted 2% and that economic activity will be boosted by tax cuts and a business-friendly administration.

The Fed Effect

    I am not suggesting that central banks don't matter or that they do not affect interest rates, because that would be an overreach, but the questions that I would like to address are about how much of an impact central banks have, and through what channels. To the first question of how much of an impact, I started by looking at the one rate that the Fed does control, the Fed Funds rate, an overnight interbank borrowing rate that nevertheless has resonance for the rest of the market. To get a measure of how the Fed Funds rate has evolved over time, take a look at what the rate has done between 1954 and 2024:

As you can see the Fed Funds was effectively zero for a long stretch in the last decade, but has clearly spiked in the last two years. If the Fed sets rates story is right, changes in these rates should cause market set rates to change in the aftermath, and in the graph below, I look at monthly movements in the Fed Funds rate and two treasury rates - the 3-month T.Bill rate and the 10-year T.Bond rate.

The good news for the "Fed did it" story is that the Fed rates and treasury rates clearly move in unison, but all this chart shows is that Fed Funds rate move with treasury rates contemporaneously, with no clear indication of whether market rates lead to Fed Funds rates changing, or vice versa. To look at whether the Fed funds leads the rest of the market, I look at the correlation between changes in the Fed Funds rate and changes in treasury rates in subsequent months. 

As you can see from this table, the effects of changes in the Fed Funds rate on short term treasuries is positive, and statistically significant, but the relationship between the Fed Funds rate and 10-year treasuries is only 0.08, and barely meets the statistical significance test. In summary, if there is a case to be made that Fed actions move rates, it is far stronger at the short end of the treasury spectrum than at the long end, and with substantial noise in predictive effects. Just as an add on, I reversed the process and looked to see if the change in treasury rates is a good predictor of change in the Fed Funds rate and obtained correlations that look very similar. 

In short, the evidence is just as strong for the hypothesis that market interest rates lead the Fed to act, as they are for "Fed as a leader" hypothesis.

    As to why the Fed's actions affect market interest rates, it has less to do with the level of the Fed Funds rate and more to do with the market reads into the Fed's actions. Ultimately, a central bank's effect on market interest rates stems from three factors:

1. Information: It is true that the Fed collects substantial data on consumer and business behavior that it can use to make more reasoned judgments about where inflation and real growth are headed than the rest of the market, and its actions often are viewed as a signal of that information. Thus, an unexpected increase in the Fed Funds rate may signal that the Fed sees higher inflation  than the market perceives at the moment, and a big drop in the Fed Funds rates may indicate that it sees the economy weakening at a time when the market may be unaware.
2. Central bank credibility: Implicit in the signaling argument is the belief that the central bank is serious in its intent to keep inflation in check, and that is has enough independence from the government to be able to act accordingly. A central bank that is viewed as a tool for the government will very quickly lose its capacity to affect interest rates, since the market will tend to assume other motives (than fighting inflation) for rate cuts or raises. In fact, a central bank that lowers rates, in the face of high and rising inflation, because it is the politically expedient thing to do may find that market interest move up in response, rather than down.
3. Interest rate level: If the primary mechanism for central banks signaling intent remains the Fed Funds rate (or its equivalent in other markets), with rate rises indicating that the economy/inflation is overheating and rate cuts suggesting the opposite, there is an inherent problem that central banks face, if interest rates fall towards zero. The signaling becomes one sided i.e., rates can be raised to put the economy in check, but there is not much room to cut rates. This, of course, is exactly what the Japanese central bank has faced for three decades, and European and US banks in the last decade, reducing their signal power.

The most credible central banks in history, from the Bundesbank in Deutsche Mark Germany to the Fed, after the Volcker years, earned their credibility by sticking with their choices, even in the face of economic disruption and political pushback. That said, in both these instances, central bankers chose to stay in the background, and let their actions speak for themselves. Since 2008, central bankers, perhaps egged on by investors and governments, have become more visible, more active and, in my view, more arrogant, and that, in a strange way, has made their actions less consequential. Put simply, the more the investing world revolves around FOMC meetings and the smoke signals that come out of them, the less these meetings matter to markets. 

Forecasting Rates

    I am wary of Fed watchers and interest rate savants, who claim to be able to sense movements in rates before they happen for two reasons. First, their track records are so awful that they make soothsayers and tarot card readers look good. Second, unlike a company's earnings or risk, where you can claim to have a differential advantage in estimating it, it is unclear to me what any expert, no matter how credentialed, can bring to the table that gives them an edge in forecasting interest rates. In my valuations, this skepticism about interest rate forecasting plays out in an assumption where I do not try to second guess the bond market and replace current treasury bond rates with fanciful estimates of normalized or forecasted rates. If you look back at my S&P 500 valuation in my second data post for this year, you will see that I left the treasury bond rate at 4.58% (its level at the start of 2025) unchanged through time.

     If you feel the urge to play interest forecaster, I do think that it is good practice to make sure that your views on the direction of interest rates are are consistent with the views of inflation and growth you are building into your cash flows. If you buy into my thesis that it is changes in expected inflation and real growth that causes rates to change in interest rates, any forecast of interest rates has be backed up by a story about changing inflation or real growth. Thus, if you forecast that the ten-year treasury rate will rise to 6% over the next two years, you have to follow through and explain whether rising inflation or higher real growth (or both) that is triggering this surge, since that diagnosis have different consequences for value. Higher interest rates driven by higher inflation will generally have neutral effects on value, for companies with pricing power, and negative effects for companies that do not. Higher interest rates precipitated by stronger real growth is more likely to be neutral for the market, since higher earnings (from the stronger economy) can offset the higher rates. The most empty forecasts of interest rates are the ones where the forecaster's only reason for predicting higher or lower rates is central banks, and I am afraid that the discussion of interest rates has become vacuous over the last two decades, as the delusion that the Fed sets interest rates becomes deeply engrained.

Corporate Bond Rates in 2024

    The corporate bond market gets less attention that the treasury bond market, partly because rates in that market are very much driven by what happens in the treasury market. Last year, as the treasury bond rate rose from 3.88% to 4.58%, it should come as no surprise that corporate bond rates rose as well, but there is information in the rate differences between the two markets. That rate difference, of course, is the default spread, and it will vary across different corporate bonds, based almost entirely on perceived default risk. 

Default spread = Corporate bond rate - Treasury bond rate on bond of equal maturity

Using bond ratings as measures of default risk, and computing the default spreads for each ratings class, I captured the journey of default spreads during 2024:

During 2024, default spreads decreased over the course of the year, for all ratings classes, albeit more for the lowest rated bonds. Using a different lexicon, the price of risk in the bond market decreased during the course of the year, and if you relate that back to my second data update, where I computed a price of risk for equity markets (the equity risk premium), you can see the parallels. In fact, in the graph below, I compare the price of risk in both the equity and bond markets across time:

In most years, equity risk premiums and bond default spreads move in the same direction, as was the case in 2024. That should come as little surprise, since the forces that cause investors to spike up premiums (fear) or bid them down (hope and greed) cut across both markets. In fact, lookin a the ratio of the equity risk premium to the default spread, you could argue that equity risk premiums are too high, relative to bond default spreads, and that you should see a narrowing of the difference, either with a lower equity premium (higher stock prices) or a higher default spread on bonds.

    The decline of fear in corporate bond markets can be captured on another dimension as well, which is in bond issuances, especially by companies that face high default risk. In the graph below, I look at corporate bond issuance in 2024, broken down into investment grade (BBB or higher) and high yield (less than BBB). 

Note that high yield issuances which spiked in 2020 and 2021, peak greed years, almost disappeared in 2022. They made a mild comeback in 2023 and that recovery continued in 2024. 

    Finally, as companies adjust to a new interest rate environment, where short terms rates are no longer close to zero and long term rates have moved up significantly from the lows they hit before 2022, there are two other big shifts that have occurred, and the table below captures those shifts:

First, you will note that after a long stretch, where the percent of bond that were callable declined, they have spiked again. That should come as no surprise, since the option, for a company, to call back a bond is most valuable, when you believe that there is a healthy chance that rates will go down in the future. When corporates could borrow money at 3%, long term, they clearly attached a lower likelihood to a rate decline, but as rates have risen, companies are rediscovering the value of having a  calculability option. Second, the percent of bond issuances with floating rate debt has also surged over the last three years, again indicating that when rates are low, companies were inclined to lock them in for the long term with fixed rate issuances, but at the higher rates of today,  they are more willing to let those rates float, hoping for lower rates in future years.

In Conclusion

    I spend much of my time in the equity market, valuing companies and assessing risk. I must confess that I find the bond market far less interesting, since so much of the focus is on the downside, and while I am glad that there are other people who care about that, I prefer to operate in a space where there there is more uncertainty. That said, though, I dabble in bond markets because what happens in those markets, unlike what happens in Las Vegas, does not stay in bond markets. The spillover effects into equity markets can be substantial, and in some cases, devastating. In my posts looking back at 2022, I noted how a record bad year for bond markets, as both treasury and corporate bonds took a beating for the ages, very quickly found its ways into stocks, dragging the market down. On that count, bond markets had a quiet year in 2024, but they may be overdue for a clean up.

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Data Updates for 2025

1. Data Update 1 for 2025: The Draw (and Danger) of Data!
2. Data Update 2 for 2025: The Party continued for US Equities
3. Data Update 3 for 2025: The times they are a'changin'!
4. Data Update 4 for 2025: Interest Rates, Inflation and Central Banks!

Data Links

1. Intrinsic risk free rates and Nominal interest rates
2. Bond Default Spreads and Equity Risk Premiums

Data Update 3 for 2025: The times they are a'changin'!

In my first two data posts for 2025, I looked at the strong year that US equities had in 2024, but a very good year for the overall market does not always translate into equivalent returns across segments of the market. In this post, I will remain focused on US equities, but I will break them into groupings, looking for differences. I first classify US stocks by sector, to see return variations across different industry groupings. I follow up by looking at companies broken down by market capitalization,  with an eye on whether the much-vaunted small cap premium has made a comeback. In the process, I also look how much the market owes its winnings to its biggest companies, with the Mag Seven coming under the microscope. In the next section, I  look at stock returns for companies in different price to book deciles, in a simplistic assessment of the value premium. With both the size and value premiums, I will extend my assessment over time to see how (and why) these premiums have changed, with lessons for analysts and investors. In the final section, I look at companies categorized by price momentum coming into 2024, to track whether winning stocks in 2023 were more likely to be winners or losers in 2024.

US Stocks, by Sector (and Industry)

       It is true that you very seldom see a market advance that is balanced across sectors and industries. This market (US stocks in 2024) spread its winnings across sectors disproportionately, with four sectors - technology, communication services, consumer discretionary and financials - delivering returns in excess of 20% in 2024, and three sectors - health care, materials and real estate delivering returns close to zero:

Sector Returns - Historical (with $ changes in millions)

The performance of technology stocks collectively becomes even more impressive, when you look at the fact that they added almost $4.63 trillion in market cap just in 2024, and that over the last five (ten) years, the sector has added $11.3 trillion ($13.6 trillion) in market cap. 

   I  break the sectors down into 93 industries, to get a finer layer of detail, and there again there are vast differences between winning and losing industry groups, based upon stock price performance in 2024:

$ changes in millions

While most of the industries on the worst-performing list represent old economy companies (steel, chemicals, rubber & tires), green energy finds itself on the list as well, perhaps because the "virtue trade" (where impact and socially conscious investors bought these companies for their greenness, rather than business models) lost its heft. The top two performers, in 2024, on the best performing industry list, semiconductors and auto & truck, owe much of their overall performance to super-performers in each one (Nvidia with semiconductors and Tesla with auto & truck), but airline companies also had a good year, though it may be premature to conclude that they have finally found working business models that can deliver profitability on a continuous basis.

US Stocks, by Market Cap

    For much of the last century, the conventional wisdom has been that small companies, with size measured by market cap, deliver higher returns than larger companies, on a risk-adjusted basis, with the debate being about whether that was because the risk measures were flawed or because small cap stocks were superior investments. That "small cap premium" has found its way into valuation practitioners playbooks, manifesting as an augmentation (of between 3-5%) on the cost of equity of small companies.  To get a sense of how market capitalization was related to returns, I classified all publicly traded US companies, by market cap, and looked at their returns in 2024.

The returns across deciles are volatile, and while the lowest deciles in terms of market cap deliver higher percent returns, looking at the top and bottom halves of the market, in terms of market cap, you can see that there is not much setting apart the two groups. 

    To make an assessment of how the performance of small cap stocks in 2024 falls in the historical spectrum, I drew on Ken French's research return data, one of my favorite data sources, and looked at the small cap premium as the difference in compounded annual returns between the lowest and highest deciles of companies, in terms of market cap:

My small cap premium spreadsheet, based on Ken French data

In this graph, you can see the basis for the small cap premium, but only if go back all the way to 1927, and even with that extended time period, it is far stronger with equally weighted than with value weighted returns; the 1927-2024 small cap premium is 2.07% with value-weighted returns and 6.69% in  equally weighted terms. It should be noted that even its heyday, the small cap premium had some disconcerting features including the facts that almost of it was earned in one month (January) of each year, and that it was sensitive to starting and end points for annual data, with smaller premium in mid-year starting points. To see how dependent this premium is on the front end of the time period, I estimated the small cap premium with different starting years in the graph (and the table), and as you can see the small cap premium drops to zero with any time period that starts in 1970 and beyond. In fact, the small cap premium has become a large cap premium for much of this century, with small cap returns lagging large cap returns by about 4-4.5% in the last 20 years.

    The market skew towards large cap companies can be seen even more dramatically, if you break stocks down by percentile, based upon market cap, and look at how much of the increase in market cap in US equities is accounted for by different percentile groupings:

US Stocks: Market Cap Change Breakdown

Looking across 6000 publicly traded stocks in 2024, the top percentile (about 60 stocks) accounted for 74% of the increase in market cap, and the top ten percent of all stocks delivered 94% of the change in total market capitalization.

    Zeroing in even further and looking at the biggest companies in the top percentile, the Mag Seven, the concentration of winners at the very top is clear:

$ changes in millions

In 2024, seven companies (Apple, Amazon, Meta, Alphabet, Microsoft, Nvidia and Tesla) increased in market cap by $5.6 trillion, almost of the entire market's gain for the year. While it is not uncommon for stock market returns to be delivered by a few winners at the top,  with the Mag Seven, the domination extends over a decade, and in the last ten years (2014-2024), these seven companies have added $15.8 trillion in market cap, about 40% of the increase in market capitalization across all US stocks over the decade.

    For years now, some investors have bet on a reversal in this trend line, with small cap stocks coming back in favor, and these investors have lagged the market badly. To get a better handle on why large cap stocks have acquired a dominant role, in markets, I look at three explanations that I have seen offered for the phenomenon:

1. Momentum story: Momentum has always been a strong force in markets, in both directions, with price increases in stocks (decreases) followed by more price increases (decreases). In effect, winning stocks continue to win, drawing in new funds and investors, but when these same stocks start losing, the same process plays out in reverse. A reasonable argument can be made that increasing access to information and easing trading, for both individual and institutional investing, with a boost from social media, has increased momentum, and thus the stock prices of large cap stocks. The dark side of this story, though, is that if the momentum ever shifted, these large cap stocks could lose trillions in value.
2. Passive investing: Over the last two decades, passive investing (in the form of index funds and ETFs) has taken market share from active investors, accounting for close to  50% of all invested funds in 2024. That shift has been driven by active investing underperformance and a surge in passive investing vehicles that are accessible to all investors. Since many passive investing vehicles hold all of the stocks in the index in proportion to their market cap, there presence and growth creates fund flows into large cap stocks and keeps their prices elevated. Here again, the dark side is that if fund flows reverse and became negative, i.e., investors start pulling money out of markets, large cap stocks will be disproportionately hurt.
3. Industry economics: In writing about the disruption unleashed by tech start-ups, especially in the last two decades, I have noted the these disruptors have changed industry economics in many established businesses, replacing splintered, dispersed competition with consolidation. Thus, Meta and Alphabet now have dominant market shares of the advertising business, just as Uber, Lyft and Grab have consolidated the car service business. As industries consolidate, we are likely to see them dominated by a few, big winners, which will play out in the stock market as well. It is possible that antitrust laws and regulatory authorities will try to put constraints on these biggest winners, but as I noted in my post on the topic, it will not be easy.

In my view, the small cap premium is not coming back, and given that it has been invisible for five decades now, the only explanation for why appraisers and analysts hold on to it is inertia. That said, the large cap premium that we have seen in the last two decades, was businesses have transitioned from splintered to consolidated structure, will also fade. Where does that leave us? Picking a company to invest in, based upon its market capitalization, will be, at best, a neutral strategy, and that should surprise no one.

The Value Premium?

    Just as the small cap premium acquired standing as conventional wisdom in the twentieth century, the data and research also indicated that stocks that trade at low price to book ratios earned higher returns that stocks that trade at high price to book ratios, in what was labeled as the value premium. As with the size premium, low price to book (value) stocks have struggled to deliver in the twenty first century, and as with the small size premium, investors have waited for it to return. To see how stocks in different price to book classes performed in 2024, I looked at returns in 2024, for all US stocks, broken down into price to book deciles:

Deciles created based on price to book ratios at start of 2024

In 2024, at least, it was the companies in the top decile (highest price to book ratios) that delivered the best returns in 2024, and stocks in the lowest decile lagged the market. 

    Here again, Ken French's data is indispensable in gaining historical perspective, as I looked the difference in annual returns between the top decile and bottom decile of stocks, classified by price to book, going back to 1927:

My value premium spreadsheet, based on Ken French data

In this graph, I am computing the premium earned by low price to book stocks, in the US, with different starting points. Thus, if you go back to 1927 and look at returns on the lowest and highest deciles, the lowest decile earned an annual premium of 2.43%. That premium remains positive until you get to about 1990, when it switches signs; the lowest price to book stocks have earned 0.87% less annually between 1990 and 2024, than the highest price to book stocks. As was the case with the small cap premium, the premium earned by low price to book stocks over high price to book stocks has faded over time, spending more time in negative territory in the last 20 years, than positive. 

    Value investors, or at least the ones that use the conventional proxies for cheapness (low price to book or low PE ratios), have felt the effects, significantly under performing the market for much of the last two decades. While some of them still hold on to the hope that this is just a phase that will reverse, there are three fundamentals at play that may indicate that the low price to book premium will not be back, at least on a sustained basis:

1. Price to book ≠ Value: It is true that using low price to book as an indicator of value is simplistic, and that there are multiple other factors (good management, earnings quality, moats) to consider before making a value judgment. It is also true that as the market's center of gravity has shifted towards companies with intangible assets, the troubles that accountants have had in putting a number on intangible asset investments has made book value less and less meaningful at companies, making it a poorer and poorer indicator of what a company's assets are worth.
2. Momentum: In markets, the returns to value investing has generally moved inversely with the strength of momentum. Thus, the same forces that have strengthened the power of momentum, that we noted in the context of the fading of the small cap premium, have diluted the power  of value investing.
3. Structural Shifts: At the heart of the premium earned by low price to book ratios is mean reversion, with much of the high returns earned by these stocks coming from moving towards the average (price to book) over time. While that worked in the twentieth century, when the US was the most mean-reverting and predictable market/economy of all time, it has lost its power as disruption and globalization have weakened mean reversion.

So, what does this mean for the future? I see no payoff in investing in low price to book stocks and waiting for the value premium to return. As with market cap, I believe that the value effect will become volatile, with low price to book stocks winning in some years and high price to book stocks in others, and investing in one or another of these groups, just on the basis of their price to book ratios, will no longer deliver excess returns.

    Since the fading of the small cap and value premiums can be traced at least partially to the strengthening of momentum, as a market force, I looked at the interplay between momentum and stock returns, by breaking companies into deciles, based upon stock price performance in the previous year (2023), and looking at returns in 2024:

Deciles formed on percentage returns in 2023

As you can see, barring the bottom decile, which includes the biggest losers of 2023, where there was a strong bounce back (albeit less in dollar terms, than in percent), there was a strong momentum effect in 2024, with the biggest winners from last year (2023) continuing to win in 2024. In short, momentum continued its dominance in 2024, good news for traders who make money in its tailwinds, with the caveat that momentum is a fickle force, and that 2025 may be the year where it reverses.

Implications

    The US equity market in 2024 followed a pathway that has become familiar to investor in the last decade, with large companies, many with a tech focus, carried the market, and traditional strategies that delivered higher returns, such as investing in small cap or low price to book stocks, faltered. This is not a passing phase, and reflects the market coming to terms with a changed economic order and investor behavior. There are lessons from the year for almost everyone in the process, from investors to traders to corporate executive and regulators:

1. For investors: I have said some harsh things about active investing, as practiced today, since much of it is based upon history and mean reversion. A mutual fund manager who screens stocks for low PE ratios and high growth, while demanding a hefty management fee, deserves to be replaced by an ETF or index fund, and that displacement will continue, pruning the active management population. For active investors who hold on to the hope that quant strategies or AI will let them rediscover their mojo, I am afraid that disappointment is awaiting them.
2. For traders: Traders live and die on momentum, and as market momentum continues to get stronger, making money will look easy, until momentum shifts. Coming off a year like 2024, where chasing momentum would have delivered market-beating returns, the market may be setting up traders for a takedown. It may be time for traders to revisit and refine their skills at detecting market momentum shifts.
3. For companies: Companies that measure their success through stock market returns may find that the market price has become a noisier judge of their actions. Thus, a company that takes a value destructive path that feeds into momentum may find the market rewarding it with a higher price, but it is playing a dangerous game that could turn against it. 
4. For regulators: With momentum comes volatility and corrections, as momentum shifts, and those corrections will cause many to lose money, and for some, perhaps even their life savings. Regulators will feel the pressure to step in and protect these investors from their own mistakes, but in my view, it will be futile. In the markets that we inhabit, literally any investment can be an instrument for speculation. After all, Gamestop and AMC were fairly stolid stocks until they attracted the meme crowd, and Microstrategy, once a technology firm, has become almost entirely a Bitcoin play. 

I recently watched Timothy Chalamet play Bob Dylan in the movie, A Complete Unknown,  and I was reminded of one of my favorite Dylan tunes, "The times they are a-changin".  I started my investing in the 1980s, in a very different market and time, and while I have not changed my investing principles, I have had to modify and adapt them to reflect a changed market environment. You may not agree with my view that both the small cap and value premiums are in our past, but it behooves you to question their existence. 

YouTube Video

Data Updates for 2025

1. Data Update 1 for 2025: The Draw (and Danger) of Data!
2. Data Update 2 for 2025: The Party continued for US Equities
3. Data Update 3 for 2025: The times they are a'changin'!
4. Data Update 4 for 2025: Interest Rates, Inflation and Central Banks!

Datasets

1. My small cap premium calculator (based on Ken French data)
2. My value premium calculator (based on Ken French data)

Data Update 2 for 2025: The Party Continued (for US Equities)

In my last post, I noted that the US has extended its dominance of global equities in recent years, increasing its share of market capitalization from 42% in at the start of 2023 to 44% at the start of 2024 to 49% at the start of 2025. That rise was driven by a surge in US equity values during 2024, with the S&P 500 delivering returns of close to 25%, all the more impressive, given that the index delivered returns in excess of 26% in 2023. In this post, I will zero in on US equities, in the aggregate, first by looking at month-by-month returns during 2024, and then putting their performance in the last two years in a historical context. I will follow up by trying to judge where markets stand at the start of 2025, starting with PE ratios, moving on to earnings yields and ending with a valuation of the index.

US Equities in 2024

    Entering 2024, there was trepidation about where stocks would go during the year especially coming off a a strong bounce back year in 2023, and there remained real concerns about inflation and a recession. The hopeful note was that the Fed would lower the Fed Funds rate during the course of the year, triggering (at least in the minds of Fed watchers) lower interest rates across the yield curve, Clearly, the market not only fought through those concerns, but did so in the face of rising treasury rates, especially at the long end of the spectrum. 

    While the market was up strongly for the year, it is worth remembering that the there were months during 2024, where the market looked shaky, as can be seen in the month to month returns on the S&P 500 during the course of 2024:

The market’s weakest month was April 2024, and it ended the year or a weak note, down 2.50% in December. Overall, though the index was up 23.31% for the year, and adding the dividend yield of 1.57% (based upon the expected dividends for 2025 and the index at the start of the years) yields a total return 24.88% for the year:

As is almost always the case, the bulk of the returns from equity came from price appreciation, with the caveat that the dividend yield portion has shrunk over the last few decades in the United States.

Historical Context

    To assess stock returns in 2024, it makes sense to step back and put the year's performance into historical perspective. In the graph below, I look at returns (inclusive of dividends) on the S&P 500 every year from 1928 to 2024. 

Download historical data

Across the 97 years that I have estimated annual returns, stocks have had their ups and downs, delivering positive returns in 71 years and negative returns in the other 26 years. The worst year in history was 1931, with stocks returning -43.84%, and the best year was 1954, when the annual return was 52.56%. If you wanted to pick a benchmark to compare annual returns to pass judgment on whether a year was above or below average, you can can go with either the annual return (11.79%) or the median return (14.82%) across the entire time period.

    Looking at the 24.88% return in 2024 in terms of rankings, it ranks as the 27th best year across the last 97 years, indicating that while it was a good year, there have been far better years for US stocks. Combining 2023 and 2024 returns yield a cumulative a two-year return for the S&P 500 of 57.42%, making it one the ten best two-year periods in US market history. 

    The riskless alternative to investing in US stocks during this period, in US dollar terms, are US treasuries, and in 2024, that contest was won, hands down, by US equities:

Equity risk premium earned in 2024, over 3-month  treasury bills 

= Return on stocks - Return on 3-month treasuries (averaged over 2024) 

= 24.88% -4.97% = 19.91%

Equity risk premium earned in 2024, over 10-year treasuries

= Return on stocks - Return on 10-year treasury

= 24.88% -(-1.64%) = 26.52%

The ten-year treasury return was negative, because treasury bond rates rose during 2024. 

    Equity risk premiums are volatile over time, and averaging them makes sense, and in the table below, I look at the premium that stocks have earned over treasury bills and treasury bonds, going back to 1928, using both simple averages (of the returns each year) and geometric averages (reflecting the compounding effect):

Download historical data

These returns are nominal returns, and inflation would have taken a bite out of returns each year. Computing the returns in real terms, by taking out inflation in each year from that year's returns, and recomputing the equity risk premiums:

Download historical data

Note that the equity risk premiums move only slightly, because inflation finds its way into both stock and treasury returns.

    Many valuation practitioners use these historical averages, when forecasting equity risk premiums in the future, but it is a practice that deserves scrutiny, partly because it is backward looking (with the expectation that things will revert back to the way they used to be), but mostly because the estimates that you get for the equity risk premium have significant error terms (see standard errors listed below the estimates in the table). Thus, if are using the average equity risk premium for the last 97 years of 5.44% (7.00%), i.e., the arithmetic or geometric averages, it behooves you to also inform users that the standard error of 2.12% will create a range of about 4% on either side of the estimate.

Pricing Questions

    Coming into 2025, investors are right to be trepidatious, for many reasons, but mostly because we are coming off two extraordinarily good years for the market, and a correction seems due. That is, however, a poor basis for market timing, because stock market history is full of examples to the contrary. There are other metrics, though, which are signaling danger, and in this section, I will wrestle with what they tell us about stocks in 2025.

PE ratios and Earnings Yields

    Even as we get new and updated pricing metrics, it is undeniable that the most widely used metric of stock market cheapness or expensiveness is the price earnings ratio, albeit with variations in the earning number that goes into the denominator on timing (current, last 12 months or trailing or next 12 month of forward), share count (diluted, primary) and measurement (ordinary or extraordinary). In the graph below, I focus on trailing earnings for all companies in the S&P 500 and compute the aggregated PE ratio for the index to be 24.16 at the start of 2025, higher than the average value for that ratio in every decade going back to 1970. 

Download data

Just for completeness, I compute two other variants of the PE, the first using average earnings over the previous ten years (normalized) and the second using the average earnings over the last ten years, adjusted for inflation (CAPE or Shiller PE). At the start of 2025, the normalized PE and CAPE also come in at well above historical norms.

    If I have terrified you with the PE story, and you have undoubtedly heard variants of this story from market experts and strategists for much of the last decade, I would hasten to add that investing on that basis would have kept you out of stocks for much of the last ten years, with catastrophic consequences for your portfolio. For some of this period, at least, you could justify the higher PE ratios with much lower treasury rates than historic norms,, and one way to see this is to compare the earnings yield, i.e., the inverse of the PE ratio, with the treasury yields, which is what I have done in the graph below:

Download data

If you compare the earnings yield to the ten-year treasury rate, you can see that for much of the last decade, going into 2022, the earnings yield, while low, was in excess of the ten-year rate. As rates have risen, though, the difference has narrowed, and at the start of 2025, the treasury rate exceeded the earnings yield. If you see market strategists or journalists talking about negative equity risk premiums, this (the difference between the earnings yield and the treasury rate) is the number that they are referencing.

    At this stage, you may be ready to bail on stocks, but I have one final card to play. In a post in 2023, I talked about equity risk premiums, and the implicit assumptions that you make when you use the earning to price ratio as your measure of the expected return on stocks. It works only if you make one of two assumptions:

1. That there will be no growth in earnings in the future, i.e., you will earn last year's earnings every year in perpetuity, making stocks into glorified bonds. 
2. In a more subtle variants, there will be growth, but that growth will come from investments that earn returns equal to the cost of equity.

The problem with both assumptions is that they are in conflict with the data. First, the earnings on the S&P 500 companies has increased 6.58% a year between 2000 and 2024, making the no-growth assumption a non-started. Second, the return on equity for the S&P 500 companies was 20.61% in 2023, and has averaged 16.38% since 2000, both numbers well in excess of the cost of equity.

    So, what is the alternative? Starting 30 years ago, I began estimating a more complete expected return on stocks, using the S&P 500, with the level of the index standing in for the price you pay for stocks, and expected earnings and cash flows, based upon consensus estimates of earnings and cash payout ratios. I solve for an internal rate of return for stocks, based upon these expected cash flows:

The expected return from this approach will be different from the earnings to price ratio because it incorporate expected growth and changes in cash flow patterns. The critique that this approach requires assumptions about the future (growth and cash flows) is disingenuous, since the earnings yield approach makes assumptions about both as well (no growth or no excess returns), and I will wager that the full ERP approach is on more defensible ground than the earning yield approach. 

    Using this approach at the start of 2025 to the S&P 500, I back out an implied expect return of 8.91% for the index, and an implied equity risk premium of 4.33% (obtained by netting out the ten-year bond rate on Jan 1, 2025, of 4.58%):

Implied ERP calculation in 2025

You are welcome to take issue with the number that I use there, lowering the growth rates for the future or changing the assumptions about payout. That is a healthy debate, and one that provides far more room for nuance that looking at the earnings yield.    

    How does an implied equity risk premium play out in market level arguments? Every argument about markets (from them being in a bubble to basement level bargains) can be restated in terms of the equity risk premium. If you believe that the equity risk premium today (4.33%) is too low, you are, in effect, stating that stocks are overvalued, and if you view it as too high, you are taking the opposite position. If you are not in the market timing business, you take the current premium as a fair premium, and move on. To provide perspective on the ERP at the start of 2025, take a look at this graph, that lists implied ERP at the start of each year going back to 1960:

Historical implied ERP

There is something here for almost point of view. If you are sanguine about stock market levels, you could point to the current premium (4.33%) being close to the historical average across the entire time period (4.25%). If you believe that stocks are over priced, you may base that on the current premium being lower than the average since 2005. I will not hide behind the "one hand, other hand" dance that so many strategists do. I think that we face significant volatility (inflation, tariffs, war) in the year to come, and I would be more comfortable with a higher ERP. At the same time, I don't fall into the bubble crowd, since the ERP is not 2%, as it was at the end of 1999. 

Valuation Questions

    Pulling together the disparate strands that are part of this post, I valued the index at the start of 2025, using the earnings expectations from analysts as the forecasted earnings for 2025 and 2026, before lowering growth rates to match the risk free rate in 2029. As the growth rates changes, I also adjust the payout ratios, given the return on equity for the S&P 500 companies:

Download spreadsheet

With the assumption that the equity risk premium will climb back to 4.5%, higher than the average for the 1960-2024 period, but lower than the post-2008 average, the value that I get for the index is about 5260, about 12% lower than the index at the start of the year. Note that this is a value for the index today, and if you wanted to adopt the market strategist approach of forecasting where the index will be a year from now, you would have to grow the value at the price appreciation portion (about 7.5%) of the expected return (which is 9.08%).

    As I see it, there are two major dangers that lurk, with the first being higher inflation (translating into higher treasury rates) and the second being a market crisis that will push up the equity risk premium, since with those pieces in play, the index becomes much more significantly over valued. From an earnings perspective, the risk is that future earnings will come in well below expectations, either because the economy slows or because of trade frictions. Rather than wring my hands about these uncertainties, I fell back on a tool that I use when confronted with change, which is a simulation:

Crystal Ball used for simulations

While the base case conclusion that the market is overvalued stays intact, not surprising since my distributions for the input variables were centered on my base assumptions, there is a far richer set of output. Put simply, at today's price levels, there is an 80% chance that stocks are overvalued and only a 20% chance that they are undervalued. That said, though, if you are bullish, I can see a pathway to getting to a higher value, with higher earnings, lower interest rates and a continued decline in the equity risk premium. Conversely, you are bearish, I understand your point of view, especially if you see earnings shocks (from a recession or a tariff war), rising inflation or a market crisis coming up.

    I don't dish out market advice, and as one whose market timing skills are questionable, you should not take my (or anyone else's) assessments at face value, especially heading into a year, where change will be the byword. It is possible that lower taxes and less regulation may cause to come in higher than expected, and that global investment fund flows will keep interest rates and equity risk premiums low. My advice is that you download the valuation spreadsheet, change the inputs to reflect your views of the world, and value the index yourself. Good investing requires taking ownership of the decisions and judgments you make, and I am glad to provide tools that help you in that process.

YouTube Video

Data Updates for 2025

1. Data Update 1 for 2025: The Draw (and Danger) of Data!
2. Data Update 2 for 2025: The Party continued for US Equities
3. Data Update 3 for 2025: The times they are a'changin'!
4. Data Update 4 for 2025: Interest Rates, Inflation and Central Banks!

Datasets

1. Historical returns on stocks: https://pages.stern.nyu.edu/~adamodar/pc/datasets/histretSP.xlsx
2. Historical implied ERP: https://pages.stern.nyu.edu/~adamodar/pc/datasets/histimpl.xls 
3. PE ratios for the S&P 500: https://pages.stern.nyu.edu/~adamodar/pc/PEforS&P500updatedJan25.xlsx

Spreadsheets 

1. Implied ERP at the start of 2025: https://pages.stern.nyu.edu/~adamodar/pc/implprem/ERPJan25.xlsx
2. Valuation of the index on Jan 1, 2025: https://pages.stern.nyu.edu/~adamodar/pc/blog/S&PValueJan2025.xlsx

Data Update 1 for 2025: The Draw (and Danger) of Data

For the last four decades, I have spent the first week of each year collecting and analyzing data on publicly traded companies and sharing what I find with anyone who is interested. It is the end of the first full week in 2025, and my data update for the year is now up and running, and I plan to use this post to describe my data sample, my processes for computing industry statistics and the links to finding them. I will also repeat the caveats about how and where the data is best used, that I have always added to my updates.

The Draw (and Dangers) of Data

   It is the age of data, as both companies and investors claim to have tamed it to serve their commercial  interests. While I believe that data can lead to better decisions, I am wary about the claims made about what it can and cannot do in terms of optimizing decision making. I find its greatest use is on two dimensions:

1. Fact-checking assertions: It has always been true that human beings assert beliefs as facts, but with social media at play, they can now make these assertion to much bigger audiences. In corporate finance and investing, which are areas that I work in, I find myself doing double takes as I listen to politicians, market experts and economists making statements about company and market behavior that are fairy tales, and data is often my weapon for discerning the truth. 
2. Noise in predictions: One reason that the expert class is increasingly mistrusted is because of the unwillingness on the part of many in this class to admit to uncertainty in their forecasts for the future. Hiding behind their academic or professional credentials, they ask people to trust them to be right, but that trust has eroded. If these predictions are based upon data, as they claim they are, it is almost always the case that they come with error (noise) and that admitting to this is not a sign of weakness. In some cases, it is true that the size of that errors may be so large that those listening to the predictions may not act on them, but that is a healthy response.

As I listen to many fall under the spell of data, with AI and analytics add to its allure, I am uncomfortable with the notion that data has all of the answers, and there two reasons why:

1. Data can be biased: There is a widely held belief that data is objective, at least if it takes numerical form. In the hands of analysts who are biased or have agendas, data can be molded to fit pre-conceptions. I would like to claim to have no bias, but that would be a lie, since biases are often engrained and unconscious, but I have tried, as best as I can, to be transparent about the sample that I use, the data that I work with and how I compute my statistics. In some cases, that may frustrate you, if you are looking for precision, since I offer a range of values, based upon different sampling and estimation choices.  Taking a look at my tax rate calculations, by industry, for US companies, int the start of 2025, I report the following tax rates across companies.

   

   Effective tax rates, by Industry (US)

   Note, that the tax rates for US companies range from 6.75% to 26.43%, depending on how I compute the rate, and which companies I use to arrive at that estimate. If you start with the pre-conception that US companies do not pay their fair share in taxes, you will latch on to the 6.75% as your estimated tax rate, whereas if you are in the camp that believes that US companies pay their fair share (or more), you may find 26.43% to be your preferred estimate. 
2. Past versus Future: Investors and companies often base their future predictions on the past, and while that is entirely understandable, there is a reason why every investment pitch comes with the disclaimer that “past performance is not a reliable indicator of future performance”. I have written about how mean reversion is at the heart of many active investing strategies, and why assuming that history will repeat can be a mistake. Thus, as you peruse my historical data on implied equity risk premiums or PE ratios for the S&P 500 over time, you may be tempted to compute averages and use them in your investment strategies, or use my industry averages for debt ratios and pricing multiples as the target for every company in the peer group, but you should hold back. 

The Sample

    It is undeniable that data is more accessible and available than ever before, and I am a beneficiary. I draw my data from many raw data sources, some of which are freely available to everyone, some of which I pay for and some of which I have access to, because I work at a business school in a university. For company data, my primary source is S&P Capital IQ, augmented with data from a Bloomberg terminal. For the segment of my data that is macroeconomic, my primary source is FRED, the data set maintained by the Federal Reserve Bank, but I supplement with other data that I found online, including NAIC for bond spread data and Political Risk Services (PRS) for country risk scores. 

    My dataset includes all publicly traded companies listed at the start of the year, with a market price available, and there were 47810 firms in my sample, roughly in line with the sample sizes in the last few years. Not surprisingly, the company listings are across the world, and I look at the breakdown of companies, by number and market cap, by geography:

As you can see, the market cap of US companies at the start of 2025 accounted for roughly 49% of the market cap of global stocks, up from 44% at the start of 2024 and 42% at the start of 2023. In the table below, we compare the changes in regional market capitalizations (in $ millions) over time.

Breaking down companies by (S&P) sector,  again both in numbers and market cap, here is what I get:

While industrials the most listed stocks, technology accounts for 21% of the market cap of all listed stocks, globally, making it the most valuable sector. Thee are wide differences across regions, though, in sector breakdown:

Much of the increase in market capitalization for US equities has come from a surging technology sector, and it is striking that Europe has the lowest percent of value from tech companies of any of the broad subgroups in this table.

    I also create a more detailed breakdown of companies into 94 industry groups, loosely structured to stay with industry groupings that I originally created in the 1990s from Value Line data, to allow for comparisons across time. I know that this classification is at odds with the industry classifications based upon SIC or NAICS codes, but it works well enough for me, at least in the context of corporate finance and valuation. For some of you, my industry classifications may be overly broad, but if you want to use a more focused peer group, I am afraid that you will have to look elsewhere. The industry averages that I report are also provided using the regional breakdown above. If you want to check out which industry group a company falls into, please click on this file (a very large one that may take a while to download) for that detail.

The Variables

    The variables that I report industry-average statistics for reflect my interests, and they range the spectrum, with risk, profitability, leverage, and dividend metrics thrown into the mix. Since I teach corporate finance and valuation, I find it useful to break down the data that I report based upon these groupings. The corporate finance grouping includes variables that help in the decisions that businesses need to make on investing, financing and dividends (with links to the US data for 2025, but you can find more extensive data links here.)

		Corporate Governance & Descriptive
		1. Insider, CEO & Institutional holdings
		2. Aggregate operating numbers
		3. Employee Count & Compensation
Investing Principle		Financing Principle		Dividend Principle
Hurdle Rate	Project Returns	Financing Mix	Financing Type	Cash Return	Dividends/Buybacks
1. Beta & Risk	1. Return on Equity	1. Debt Ratios & Fundamentals	1. Debt Details	1. Dividends and Potential Dividends (FCFE)	1.Buybacks
2. Equity Risk Premiums	2. Return on (invested) capital	2. Ratings & Spreads	2. Lease Effect	2. Dividend yield & payout
3. Default Spreads	3. Margins & ROC	3. Tax rates
4. Costs of equity & capital	4. Excess Returns on investments	4. Financing Flows
	5. Market alpha

(If you have trouble with the links, please try a different browser)

Many of these corporate finance variables, such as the costs of equity and capital, debt ratios and accounting returns also find their way into my valuations, but I add a few variables that are more attuned to my valuation and pricing data needs as well.

	Valuation		Pricing
Growth & Reinvestment	Profitability	Risk	Multiples
1. Historical Growth in Revenues & Earnings	1. Profit Margins	1. Costs of equity & capital	1. Earnings Multiples
2. Fundamental Growth in Equity Earnings	2. Return on Equity	2. Standard Deviation in Equity/Firm Value	2. Book Value Multiples
3. Fundamenal Growth in Operating Earnings			3. Revenue Multiples
4. Long term Reinvestment (Cap Ex & Acquisitons)			4. EBIT & EBITDA multiples
5. R&D
6. Working capital needs

(If you have trouble with the links, please try a different browser)

Not that while much of this data comes from drawn from financial statements, some of it is market-price driven (betas, standard deviations, trading data), some relates to asset classes (returns on stocks, bonds, real estate) and some are macroeconomic (interest rates, inflation and risk premiums).  While some of the variables are obvious, others are subject to interpretation, and I have a glossary, where you can see the definitions that I use for the accounting variables. In addition, within each of the datasets (in excel format), you will find a page defining the variables used in that dataset. 

The Timing

    These datasets were all compiled in the last four days and reflect data available at the start of 2025. For market numbers, like market capitalization, interest rates and risk premiums, these numbers are current, reflecting the market's judgments at the start of 2025. For company financial numbers, I am reliant on accounting information, which gets updated on a quarterly basis. As a consequence, the accounting numbers reflect the most recent financial filings (usually September 30, 2024), and I use the trailing 12-month numbers through the most recent filing for flow numbers (income statement and cash flow statements) and the most recent balance sheet for stock numbers (balance sheet values). 

    While this practice may seem inconsistent, it reflects what investors in the market have available to them, to price stocks. After all, no investor has access to calendar year 2024 accounting numbers at the start of 2025, and it seems entirely consistent to me that the trailing PE ratio at the start of 2025 be computed using the price at the start of 2025 divided by the trailing income in the twelve months ending in September 2024. In the same vein, the expected growth rates for the future and earnings in forward years are obtained by looking at the most updated forecasts from analysts at the start of 2025. 

    Since I update the data only once a year, it will age as we go through 2025, but that aging will be most felt, if you use my pricing multiples (PE, PBV, EV to EBITDA etc.) and not so much with the accounting ratios (accounting returns). To the extent that interest rates and risk premiums will change over the course of the year, the data sets that use them (cost of capital, excess returns) allow for updating these macro numbers. In short, if the ten-year treasury rate climbs to 5% and equity risk premiums surge, you can update those numbers in the cost of capital worksheet, and get updated values.

The Estimation Process

    While I compute the data variables by company, I am restricted from sharing company-specific data by my raw data providers, and most of the data I report is at the industry level. That said, I have wrestled with how best to estimate and report industry statistics, since almost every statistical measure comes with caveats. For a metric like price earnings ratios, computing an average across companies will result in sampling bias (from eliminating money-losing firms) and be skewed by outliers in one direction (mostly positive, since PE ratios cannot be negative). Since this problem occurs across almost all the variables, I use an aggregated variant, where with PE, for instance, I aggregate the market capitalization of all the companies (including money losing firms) in an industry grouping and divide by the aggregated net income of all the companies, including money losers. 

    Since I include all publicly traded firms in my sample, with disclosure requirements varying across firms, there are variables where the data is missing or not disclosed. Rather than throw out these firms from the sample entirely, I keep them in my universe, but report values for only the firms with non-missing data. One example is my data on employees, a dataset that I added two years ago, where I report statistics like revenue per employee and compensation statistics. Since this is not a data item that is disclosed voluntarily only by some firms, the statistics are less reliable than on where there is universal disclosure. 

    On an upbeat note,  and speaking from the perspective of someone who has been doing this for a few decades, accounting standards around the world are less divergent now than in the past, and the data, even in small emerging markets, has far fewer missing items than ten or twenty years ago. 

Accessing and Using the Data

    The data that you will find on my website is for public consumption, and I have tried to organize it to make it easily accessible on my webpage. Note that the current year’s data can be accessed here:

https://pages.stern.nyu.edu/~adamodar//New_Home_Page/datacurrent.html

If you click on a link and it does not work, please try a different browser, since Google Chrome, in particular, has had issues with downloads on my server.

    If you are interested in getting the data from previous years, it should be available in the archived data section on my webpage:

https://pages.stern.nyu.edu/~adamodar//New_Home_Page/dataarchived.html

This data goes back more than twenty years, for some data items and for US data, but only a decade or so for global markets.

       Finally, the data is intended primarily for practitioners in corporate finance and valuation, and I hope that I can save you some time and help in valuations in real time. It is worth emphasizing that every data item on my page comes from public sources, and that anyone with time and access to data can recreate it.  For a complete reading of data usage, try this link:

https://pages.stern.nyu.edu/~adamodar//New_Home_Page/datahistory.html

If you are in a regulatory or legal dispute, and you are using my data to make your case, you are welcome to do so, but please do not drag me into the fight.  As for acknowledgements when using the data, I will repeat that I said in prior years. If you use my data and want to acknowledge that usage, I thank you, but if you skip that acknowledgement, I will not view it as a slight, and I certainly am not going to threaten you with legal consequences.

    As a final note, please recognize that this I don't have a team working for me, and while that gives me the benefit of controlling the process, unlike the pope, I am extremely fallible. If you find mistakes or missing links, please let me know and I will fix them as quickly as I can. Finally, I have no desire to become a data service, and I cannot meet requests for customized data, no matter how reasonable they may be. I am sorry!

YouTube Video

Links

1. Current data (start of 2025)
2. Archived data (from prior years)
3. Companies/Industries
4. Data definitions

Data Updates for 2025

1. Data Update 1 for 2025: The Draw (and Danger) of Data!
2. Data Update 2 for 2025: The Party continued for US Equities
3. Data Update 3 for 2025: The times they are a'changin'!
4. Data Update 4 for 2025: Interest Rates, Inflation and Central Banks!