A “Fairly Highly Valued” Market: A Fed Chair Opines on Stocks, but should we listen?

     In December 1996, Alan Greenspan used the words "irrational exuberance" to describe the stock market at the time, and those words not only became the title of Robert Shiller's cautionary book on market bubbles, but also the beginnings of the belief that central bankers had the wisdom to be market timers and the power to bend the economy to their views. I think that Greenspan's words seem prophetic, only with the benefit of hindsight, and I believe that central bankers have neither the power nor the tools to move the economy in significant ways. I was reminded of that episode when I read that Jerome Powell, the current Fed chair, had described the market as "fairly highly valued". In market strategy speak, these are words that are at war with each other, since markets can either be “fairly valued” or “highly valued”, but not both, but I don't blame Powell for being evasive. For much of this year, and especially since April, the question that market observers and investors have faced is whether stocks, especially in the United States, are pushing into “bubble” territory and headed for a correction. As someone who buys into the notion that market timing is the impossible dream, you may find it surprising that I think that Powell is  right in his assessment that stocks are richly priced, but that said, I will try to explain why making the leap into concluding that stocks are in a bubble, and acting on that conclusion are much more difficult to do.

Financial Markets in 2025

    It has, to put it mildly, been an interesting year for stocks, as economic headwinds and shocks have mounted, with tariffs, wars and politics all adding to the mix. After a first quarter, where it looked like financial markets would succumb to the pressure of bad news, stock markets have come roaring back, surprising market experts and economists. As a precursor to answering the question of whether stocks are "fairly highly valued" today, let’s take a look at how we got to where we are on September 30, 2025.

Resilient Equities

    We will start with US equities, and while that may seem parochial, it is worth remembering that they represented more than 50% of the total market capitalization of all traded stocks in the world at the start of 2025. In the figure below, we look at the S&P 500 and the NASDAQ, with the former standing in as a rough proxy for large US market cap stocks and the latter for technology companies:

As you can see, US equities were down in the first quarter, but the standardized values indicate that it was much worse for technology companies than for the rest of the market, with the NASDAQ down 21.3% through April 8, the market bottom, while the S&P 500 was down 14.3%. On April 8, the consensus wisdom was that the long-awaited correction was upon us, and that tech stocks would take more of a beating over the rest of the year. The market, of course, decided to upend expectations, as tech came roaring back in the second and third quarters, carrying the market with it. In fact, through the first three quarters, the NASDAQ has reclaimed the lead, up 17.3% so far this year, whereas the S&P 500 is up 13.7%.

    We take a closer and more detailed look at all publicly traded US equities, in the table below, where we break out the year-to-date performance, by sector:

The two best performing sectors in the first three quarters of 2025 have been technology (up $3.93 trillion and 22.4% YTD) and communication services (up $1.29 trillion and 22.3% YTD). There are five sectors which lagged the market, with consumer staples and health care effectively flat for the year, and energy consumer discretionary and real estate up only 4-6% for the year. Financial, industrials and materials, for the most part, matched the overall market in terms of percentage change, and the overall value of US equities increased by $8.3 trillion (13.76%) in the first nine months of 2025. If you puzzled by the outperformance of communication services, it is worth noting that Alphabet and Meta, both of which derive large portions of their revenues from online advertising, are categorized by S&P as communication service companies. These two companies are part of the Mag Seven. and the companies in this grouping have been the engine driving US equities for much of the last decade, leading to talk of a top-heavy market. To assess their contribution to market performance, we looked at  the aggregate market cap of the seven companies, relative to all 5748 traded US equities in 2023, 2024 and 2025 (YTD):

The aggregate market capitalization of the Mag Seven, as a percent of market cap of all traded US companies, has risen from 17.5% at the end of 2022 to 24.6% at the end of 2023 to 29.3% at the end of 2024. Focusing just on 2025, the Mag Seven took a step back in the first quarter, dropping to 26.3% of overall market cap on March 31, 2025, but has made a decisive comeback since, with an increase in market cap of $2.8 trillion in the first nine months of 2025, accounting for 52.4% of the overall increase in market capitalization this year. In fact, the Mag Seven now command 30.35% of the total market capitalization for US equities, a higher percent than at the start of the year. Over the last three years, the Mag Seven alone have accounted for more than half of the increase in market capitalization of all US equities, each year.

    There are other dimensions on which you can slice and dice US equities, and we did a quick run through some of them, by breaking US companies into groupings, based upon characteristics, and examining performance in each one:

1. Small cap versus Large cap: For much of the large century, small cap stocks (especially those in the bottom decile of market capitalization) delivered higher returns than large cap stocks. As I argued in a post from a decade ago, the small cap premium has not just disappeared since the 1980s, but been replaced with a large cap premium. Looking at returns in 2025, broken down by market capitalization at the start of the year, here is what we see:
   
   As you can see, this has been a good year for small cap stocks, with the bottom half of the market seeing a much bigger increase, in percent terms, in market cap than the top half of the market, with much of the outperformance coming in the third quarter.
2. Value versus growth: Another enduring finding from the last century is that low price to book stocks delivered higher returns, after adjusting for risk, than high price to book stocks. While this is often categorized as a value effect, it works only if you accept price to book as a proxy for value, but even that effect has largely been absent in this century. Breaking down stocks based upon price to book ratios at the start of 2025, here is what we get:
   
   While it is too early to celebrate the return of value, in 2025, low price to book stocks have done better than high price to book stocks, but all of the outperformance came in the first quarter of the year.
3. Momentum: Momentum has been a stronger force in markets than either market cap or value, and unlike those two, momentum has not just maintained its edge, but strengthened it over the last few years. Using the price change in 2024 as a proxy for momentum, we broke companies down into deciles and looked at returns in 2025:
   
   After lagging in the first quarter, momentum stocks have made a comeback, with the top half of momentum stocks now leading the bottom half for the year to date in percent change in market capitalization.

In sum, it has been a good year, so far, for US equities, but the gains have been unevenly distributed across the market, and while the first quarter represented a break from the momentum and tech driven market of 2023 and 2024, the second and third quarters saw a return of those forces.

Directionless Treasuries

    While interest rates are always a driver of stock prices, they have played less of a role in driving equity markets in 2025 than in prior years. To see why, take a look at US treasury rates, across maturities, in 2025:

Rates have for the most part are close to where they were at the start of the year, with very little intra-year volatility notwithstanding economic stories about inflation and real growth suggesting bigger moves. The battle between the Trump administration and the Federal Reserve has received a great deal of press attention, but the Fed's inaction for much of the year and lowering of the Fed Funds rate in September seem to have had little or no impact on treasury rates.

    On May 16, 2025, Moody's lowered the ratings for the United States from Aaa to Aa1, joining Fitch and S&P, but again the effect on treasury rates was transient. If you are wondering why this did not translate into an increase in default spreads (and rates), the likely answer is that markets were not surprised by the downgrade, and the best evidence for this is in the 5-year US sovereign CDS spread, a market-set number for default risk (spreads):

As you can see there was a spike in the US sovereign CDS spread this year, but it happened in response to liberation day on March 31, when President Trump announced punishing tariffs on the rest of the world. The Moody's downgrade had little impact on the spread, and even the tariff effect had fully faded by September 30, 2025, with spreads back to where they were at the start of the year (and for much of the last few years).

    Extending the assessment of default spreads to the corporate market, there has been relatively little movement in corporate default spreads in 2025:

Source: FRED

As you can see, the most striking part of the story is that so little has changed over the course of 2025, notwithstanding the spike in spreads in the first week of April, when the tariffs were announced. The Moody's rating and the talk of a recession seem to have done little to supercharge the fear factor, and by extension the spreads. In fact, the only rating that has seen a significant move is in the CCC and below grouping, where spreads are now higher than they were at the start of the year, but still much lower than they were at the end of the first quarter of 2025.

The Rest of the Story

    The economic shocks that hit the US markets, and which US equities and debt shrugged off, for the most part, also reverberated in the rest of the world. The broadest measure of relative performance between US and global equities is the divergence between the S&P 500, a proxy for US equity performance, and the MSCI World index, a stand-in for large cap international stocks, and the results are below:

In the first nine months of 2025, the MSCI global equity index is up 16.6%, about 2.3% more than the S&P 500 over the same period. However, all of this underperformance occurred in the first quarter of 2025, and the S&P 500 has found its winning ways again in the second and third quarters.

    The MSCI index does obscure differences across regions and is titled towards large cap stocks. Consequently, we looked at all publicly traded equities, broken down by regions, with the values in US dollars, and the results so far in 2025 are in the table below:

Global equities were up, in aggregate dollar market capitalization, by 16.8%, and while US equities have underperformed in the first nine months of 2025, with a 13,8% return,  they have rediscovered their mojo in the second and third quarters. The worst performing regions of the world are India, down 3.15%, in US dollar terms, this year, and Africa and the Middle East, up only 2.13%. It is too early to spin stories for why these regions underperformed, but in my data update post from the start of 2025, I pointed to India as the most highly priced market in the world, and this year may reflect a cleaning up. The rest of the world ran ahead of the United States, with some of the additional return coming from a weaker US dollar; the local currency returns in these regions were lower than the returns you see in the table.

US Equities: Overpriced or Underpriced?

    None of the discussion above answers the question that we started this post with, which is whether US equities are overpriced. To make that assessment, there are a variety of metrics that are used, and while all of them are flawed, they vary in terms of what they leave out of the assessment, and the assumptions that underlie them.

At one end of the spectrum, the simplest and most incomplete metric is based purely on price history, with markets that have had extended good runs being viewed as overpriced. A modification is to bring earnings into the assessment, with prices moving disproportionately more than earnings (resulting in higher or lower PE ratios) considered a signal of market mispricing. The third adaptation allows for the returns you can make on alternative investments, in the form of interest rates on treasuries, to make a judgment on market pricing. The final and fullest variant considers growth in the assessment, bringing in both its good side (that it increases earnings in future periods) and its bad side (that it needs a portion of earnings to be reinvested), to make a pricing judgment, but even that variant ignores disruptions that alter market dynamics and risk taking.

1. Rising stock prices

    For some investors, an extended stretch of rising stock prices is, by itself, sufficient reason to conclude that if stocks are doing so well, they must be over priced. This concern will get deeper as the market run gets longer (in terms of time) and steeper (in terms of price rise). Using that framework, you can see why talk of a stock market bubble has built up over the last decade, as stocks keep climbing walls of worry and hitting new highs.  We have had a remarkable bull run in US equities over the last 15 years, with the S&P 500 up over 500% over that period:

Download spreadsheet with historical returns on stocks

In short, the annual return (18.74%) that equity investors have earned over the last fifteen years is significantly higher than the annual return (9.94%) on US equities over the last century. For some, this run-up alone is enough to decide that equities are overpriced and incomplete though this analysis is, you can see its draw for many investors.

2. The Earnings Effect

    Looking at rising stock prices as an indicator of overpricing ignores the reality that markets can sometimes be up strongly, not because of speculation or over pricing, but because of rising earnings. That is the reason that many investors look at market pricing scaled to earnings, or PE ratios, and the graph below captures three variants of the PE ratio - the trailing PE, where you scale market pricing to earnings in the last twelve months, a normalized PE, where you scale the market pricing to average earnings over a longer time period (a decade) and a CAPE or Shiller PE, where you first adjust earnings for inflation and then normalize:

Download historical PE ratios for US equities

All three versions of the PE ratio tell the same story, and in September 2025, all three stood close to all time highs, with the spike at the peak of the dot com boom being the only exception. 

3. The Investing Alternatives

    Stocks that trade at higher multiples of earnings are obviously more expensive than when then trade at lower, but to make a judgment on whether they are overpriced, you still have to compare them to what you can make on alternative investments. For investors in financial assets, those alternative investments are bonds (if you are investing long term) or commercial paper/treasury bills (if you are investing short term). Logically, if these alternatives are yielding low returns, you should be willing to pay a much higher multiples of earnings for risky assets (like stocks). One way in which we can bring in this choice is by flipping the PE ratio (to get the earning to price ratio or earnings yield) and comparing that earning yield to the ten-year treasury bond rate:

Between 2011 and 2020, for instance, the earnings yield was 5.46% but that was much higher than the 10-year treasury bond rate, which averaged 2.15% over that decade. In 2021, the earnings yield dropped to 4.33%, close to a historical low, butt with the treasury bond rate at 1.51%, you could argue that equity investors had nowhere else to go. As treasury bond rates climbed back towards 4% in 2022, stock prices dropped and the earnings yield climbed to 5.72%. In the last three years (2023-25), treasury rates have stayed higher (4% or more), but earnings yields have dropped. In fact, the earnings yield of 4% in September 2025 was 0.16% below the ten-year treasury bond rate, triggering bearish warnings from analysts who use the difference between the earnings yield and the ten-year bond rate as their market timing metric.

4. The Rest of the Story - Cash flows, Growth and Risk

    The earnings yield, in conjunction with the treasury bond rate, is widely used as a market timing tool, but it has two, perhaps fatal, flaws. 

• The first is that it treats stocks as if they were glorified bonds, treating the earnings yield like a coupon, and misses the reason that investors are drawn to equities, which is the potential for growth. Incorporating growth into the analysis has two effects, with the first being that you need reinvestment to grow, and that reinvestment comes out of earnings, and the second being the upside of increasing earnings over time. 
• The second is that the earnings yield/ treasury bond rate differential has had a spotty record timing the market, missing much of the great bull market of the 1980s and 1990s, and clearly not providing much predictive power in the last two years.

There is an approach that you can use to incorporate the growth and cash flow effects into your market analysis. It is to estimate an intrinsic value for the market, where you incorporate the growth and reinvestment effects into expected cash flows, and discount them at a required return that incorporates what you can earn on a riskfree (or close to riskfree) investment and a risk premium for investing in equities.

As you can see, the intrinsic value equation can be used in one of two ways to assess the market. One is to back out an internal rate of return, i.e., a discount rate that yields a present value equal to the market index; netting out the treasury bond rate from this yields an implied equity risk premium for the market. The other is choose an equity risk premium that you believe is reasonable and to value the market. 

    I estimate an implied equity risk premium for the S&P 500 at the start of every month, and use it as my barometer of the market, a receptacle of market hopes and fears, falling in good times and rising during crises. By my computation, the expected return on the index at the end of September 2025 was 8.17%, and with the ten-year treasury rate of 4.16% netted from it yields an implied equity risk premium of 4.01%. The question of whether the market is over or underpriced can be reframed as one about whether the equity risk premium is too low (indicating an overpriced market) or too high (underpriced market). In the figure below, I put the September ERP into perspective by comparing it to implied equity risk premiums for the S&P 500 going back in time:

Download historical implied equity risk premiums for S&P 500

As is often the case with historical comparisons, there is something here for every side of the debate. For those who believe that the market is overpriced, the obvious comparison is to equity risk premiums since the 2008 crisis, and the conclusion would be that the Sept 2025 premium of 4.01% is too low (and stock prices are too high). For those who are more sanguine about the market, the comparison would be to the dot-com boom days, when the implied equity risk premium dipped to 2%, to conclude that this market is not in a bubble. 

    An alternate way to assess market pricing is to assume an equity risk premium and estimate the value of the index using that premium. Thus, if we assume that the average premium  (4.25%) from 1960 to 2024 is a fair premium to the market, and revalue the index, here is what we would get as its value:

Download intrinsic value estimator

With an implied equity risk premium of 4.25%, and a riskfree rate of 4.16%, we get an expected return on stocks of 8.41%, and using analyst estimates of growth in earnings and cash payout ratios that adjust over time to sustainable levels, we arrive at a value for the index of about 5940, 12.6% lower than the index value on September 30, 2025.

The Market Timing Challenge

    It is undeniable that this market is richly priced on every metric, from PE ratios to the earnings yield, net of treasuries, to intrinsic value measures like the equity risk premium, thus providing backing for Powell's assessment of equities as “fairly highly valued”. If you trust in mean reversion to historical averages, it seems reasonable to conclude that stocks are in fact overpriced, and due for a correction. In this section, we will examine why, even if you come to this conclusion, it is difficult to convert it into action.

    Using lawyerly language, let's stipulate that markets are overpriced today, though that overpricing can cover a range of views from the market being a bubble to the markets just being expensive. There are five responses that you can have to this judgment, ranging from least aggressive to most aggressive on the market timing front:

1. Do nothing: The essence of being a non-market timer is that you do not alter any aspect of your portfolio to reflect your market views. Thus, if your preferred allocation mix is 60% in stocks and 40% in your bonds, you stay with that mix, and you not only hold on to your existing investments but you continue to add to them in the same way that you have always done.
2. Hold on to/ build cash holdings: For the most part, you match what you would have done in the do nothing response in terms of overall asset allocation mix and holdings, but you not only put your portfolio additions into cash (treasury bills, money market funds) but when you act, it will be more likely to be selling existing holdings (that you view as over valued) than buying new ones. For many equity mutual fund managers, this statistic (liquid assets and cash as a percent of assets under management) is a rough proxy of how bullish or bearish they are about the overall market.
3. Change asset allocation mix: In this response, you revisit your preferred asset allocation mix, which was set based on your age, cash needs and risk aversion, and alter it to reflect your market timing views. Thus, if you believe that stocks are overpriced, but you view bonds as fairly or even under priced, you will decrease your allocation to the former, and increase your allocation to the latter. If you are constrained to be an all-equity investor, an alternate version will be to reallocate your money from overpriced geographies to underpriced geographies, if the latter exist.
4. Buy protection: With the growth of options and futures markets, you now have ways of protecting your portfolio, without making wrenching changes to your asset allocation or holdings mix. You can buy puts on the index or sell index futures, if you think equities are overpriced, and benefit from the fact that the profits from these positions will offset the losses on your portfolio, if there is a correction. 
5. Make leveraged bets of market correction: The most aggressive way to take advantage of market timing is to make leveraged bets on market corrections, using either derivatives markets (puts or futures) or selling short on either all of the stocks in an index, or a subset of the most overpriced. 

In making this choices, you do have to consider three real world concerns. The first is taxes, with any strategies that requires significant disruptions to existing portfolios, such as changing asset allocation mixes or selling overvalued stocks in the portfolio, creating larger tax bills. The second is transactions costs, which will also be higher for any strategy that is built around more aggressive. The third is timing, which is that even if you are right about the overpricing, being right too early may wipe out the benefits. Speaking of Alan Greenspan's warnings about the dot com bubble, it is worth remembering that his "irrational exuberance" comments were made in 1996, and that the market correction occurred in 2001, and any investor who sold equities right after the comments were made would have underperformed an investor who held on to equities and took the hit from the correction.

    Let's assume that you remove taxes and transactions costs from the assessment to give market timing the best possible pathway to success. To test whether market timing works, you have to create a market timing strategy around your metric of choice, with three steps fleshed out:

1. Choose your pricing metric: As noted in the last section, this can be the percentage increase in stock prices over a recent period, the current or normalized pricing ratio (PE, PBV, EV to EBITDA) or the equity risk premium/intrinsic value for the index.
2. Create your action rule: The action rule specifies the threshold for the chosen metric, where you will act on your market timing. You could, for instance, decide that you will increase your equity exposure if the PE ratio is more than 25% below the median value for the market's PE ratios over the previous 25, 50 or 100 years, and reduce your equity exposure if the PE ratio is more than 25% higher than the median value over the period. Note that  the trade off on setting the threshold is that setting it to a larger value (say 50%) will mean that you time the market less.
3. Choose your market timing response: You specify how much you will increase or decrease your equity exposure in response to the market timing signal. Thus, if you have base asset allocation mix of 60% equities, 40% bonds, you can decide that if your threshold (from step 2) is breached, and the PE ratio drops (increases) by more than 25% below the median, your equity exposure will increase (decrease) to 80% (40%) and your bond exposure reduced (increased) to 20% (60%). The more aggressive you are as a market timer, the greater will be the shift away from your base mix. Thus, you could sell all equities (0% equities, 100% bonds) if the market is overpriced and hold only equities (100% equities, 0% bonds) if the market is underpriced.

To illustrate, let's use the Shiller PE, pick a 25% threshold for market cheapness and  alter your asset allocation mix, which would normally be 60% equity/40% debt to 80% equities/20% debt if the Shiller PE drops 25% below the median (computed over the prior 50 years) and 40% equities/ 60% bonds if it rises 25% above the median. 

Note that the test can easily be varied, using a different metric, different thresholds and different timing responses.

    To avoid being accused of cherry picking the data or deviating from the standard measures of the Shiller PE, I downloaded the raw data on stock returns, bonds and the CAPE each year from 1871-2025 from Shiller's own webpage. To compute the payoff to market timing, I looked at the annual returns to an non-market-timing investor who stayed with a 60% equity/40% bond mix over time and compared it to the returns of a market timer, using the threshold/action strategy described above:

Download CAPE backtesting spreadsheet

Over the last century, this market timing strategy would have reduced your annual returns 0.04% each year, and that is before transactions costs and taxes. If you break this up into two half-centuries, any of the market timing gains were from 1924-1974, and they were mild, and trying to time the market would have reduced your annual returns by 0.41% a year, on average between 1975 and 2024.

    To evaluate whether the payoff would have been different with alternate thresholds, we considered both a much lower threshold (10%) and a much higher one (50%), with the former leading to more market timing actions. We also looked at a more aggressive response, where the equity portion was increased to 100% (instead of 80%) if the market was underpriced and reduced to 0% (instead of 40%) if the market was overpriced. The results are in below:

Download CAPE backtesting spreadsheet

As you can see in this table, there is not a single market timing combination (threshold and action) that would have added to annual returns over the last fifty years. I completely understand that there are other combinations that may work, and you are welcome to download the spreadsheet and try for yourselves, changing the threshold levels for actions and the action itself. You may very well find a combination that adds value but the fact that you have try this hard is indicative of why market timing is a reach.  It is also possible that making these timing judgments only once a year may be getting in the way of them working, but I did use the monthly data that Shiller has accessible, and in my experimenting, there was little that I could see in terms of added value.

Conclusion

    The decision on whether to time markets is a personal one, and while I have concluded it does not work for me, it would be presumptuous to claim that it will not work for you. If you decide that market timing is part of your investment philosophy, though, there are three lessons that I hope that this post has highlighted. The first is that the more incomplete your market timing metrics are, the greater the chance that you will chasing a correction that never happens. It is the reason that you should be skeptical about arguments built around just pricing, PE ratios or earnings yields (relative to treasury bond rates), and even with more complete metrics, you should be scanning the horizon for fundamental changes in the economy and markets that may explain the deviation. The second is that the proof that a metric will work for you will not come from statistical measures (correlations and regressions), but from creating and back-testing an actionable strategy (of buying or selling traded instruments) based on the metric. The third is that even if you do all of this due diligence, market timing is noisy and flawed, and paraphrasing another widely used expressions, markets can stay mispriced for longer than you can stay solvent. 

YouTube Video

Datasets

1. Historical returns on stocks, bonds, bills, real estate and gold: 1927 - 2024
2. PE, Normalized PE, Shiller PE and Earnings Yield Data for US Stocks: 1960-2025
3. Shiller data on stock returns, interest rates and CAPE (monthly): 1871-2024 (from Shiller)
4. Implied Equity Risk Premiums for the S&P 500: 1960-2025

Spreadsheets

1. Backtester for CAPE-based market timing strategies
2. Implied equity risk premium calculator 
3. Intrinsic value for the S&P 500

The Imitation Game: Defending against AI's Dark Side!

    A few weeks ago, I started receiving a stream of message about an Instagram post that I was allegedly starring in, where after offering my views on Palantir's valuation, I was soliciting investors to invest with me (or with an investment entity that had ties to me). I was not surprised, since I have lived with imitations for years, but I was bemused, since I don't have an Instagram account and have not posted on Facebook more than once or twice in a decade. In the last few days, those warnings have been joined by others, who have noted that there is now a video that looks and sounds like me, adding to the sales pitch with promises of super-normal returns if they reach out, and presumably send their money in. (Please don't go looking for these scams online, since the very act of clicking on them can expose you to their reach.)

    I would like to think that readers of my books or posts, or students in my classes, know me well enough to be able to tell that these are fakes, and while this is not the first time I have been targeted, it is clear that AI has upped the ante, in terms of creating posts and videos that look authentic. In response, I cycled through a series of emotions, starting with surprise that there are some out there who think that using my name alone will draw in investors, moving on to anger at the targeting of vulnerable investors and ending with frustration at the social media platforms that allow these fakes to exist. As a teacher, though, curiosity beat out all of these emotions, and I thought that the best thing that I can do, in addition to the fruitless exercise of notifying the social media companies about the fakes, is to talk about what these AI imitators got right, what they were off target on and what they got wrong in trying to create these fakes of me. Put simply, I plan to grade my AI imitator, as I would any student in my class, recognizing that being objective in this exercise will be tough to do. In the lead-in, though, I have to bore you with details of my professional life and thought process, since that is the key to creating a general framework that you will be able to use to detect AI imitations, since the game will only get more sophisticated in the years to come.

An Easy Target?

    In a post last year, I talked about a bot in my name, that was in development phase at NYU, and while officially sanctioned, it did open up existential challenges  for me. In discussing that bot, I noted that this bot had accessed everything that I had ever written, talked about or valued in my lifetime, and that I had facilitated its path by making that access easy. I will explain my rationale for the open access, and provide you with the links if you want to get to them, hoping to pre-empt those who will try to charge you for that content.

My Open Access Policy

    I have said this before, but there is no harm in saying it again, but I am a teacher, first and foremost, and almost every choice I make in my profession life reflects that mindset. A teacher, like an actor or singer, craves an audience, and the larger and more enthusiastic that audience, the better. When I started teaching in 1986, my audience was restricted to those in my physical classroom at NYU's business school, and my initial attempts at expanding that audience were very limited. I had video recorders set up to record my lectures, made three copies of each lecture tape, and put them on the shelves at NYU's library for patrons to check out and watch. The internet, for all of its sins, changed the game for me, allowing me to share not only class materials (slides, exams) but also my lecture videos, in online formats. Though my early attempts to make these conversions were primitive, the technology for recording classes and putting them online has made a quantum leap. In spring 2025, every one of my NYU classes was recorded by cameras that are built into classroom, the conversions to online videos happened in minutes, right after the class is done, and YouTube has been a game changer, in allowing access to anyone with an internet connection anywhere in the world.

    As the internet has expanded its reach, and social media platforms have joined the mix, I have also shared the other components that go into my classes more widely, starting with the data on industry averages that I need and use in my own valuations, the spreadsheets that contain these valuations and blog posts on markets and companies and any other tools that I use in my own analyses. While I am happy to receive compliments for the sharing and praise for being unselfish, the truth is that my sharing is driven less by altruism (I am no Mother Theresa!) and more  by two other forces. The first is that, as I noted in my post on country equity risk premiums last week, there much of what I know or write about is pedestrian, and holding it in secret seems silly. The second is that, while I am not easily outraged, I am driven to outrage by business consultants and experts who state the obvious (replacing words you know with buzzwords and acronyms), while making outrageous claims of what they can deliver and charging their customers absurd amounts for their advice and appraisals. If I can save even a few of these customers from making these payments, I consider it to be a win.

My Sharing Spots

    Everything that I have ever written, worked on or taught is somewhere online, almost always with no protective shields (no passwords or subscriptions), and there are four places where you can find them:

• Webpage: The oldest platform for my content remains my webpage, damodaran.com, and while it can be creaky, and difficult to navigate, it contains the links to my writing, teaching, data, spreadsheets and other tools. 
• Teaching: I teach two classes at Stern, corporate finance and valuation, and have four other classes - a lead-into-valuation accounting class, a made-for-finance statistics class, a class on investment philosophies and one on corporate life cycles, and I described these classes in a post on teaching at the start of 2025. You can find them all by going to the teaching link on my webpage, https://people.stern.nyu.edu/adamodar/New_Home_Page/teaching.html including my regular classes (class material, lecture notes, exams and quizzes and webcasts of the classes) in real time, as well as archived versions from previous semesters. In addition, the online classes are at the same link, with material, post- class tests and webcasts of sessions for each class. This is also the place where you can find links to seminars that I teach in the rest of the world, with slides and materials that I used for those classes (though I have been tardy about updating these).
• Data: At the start of every year for the last three decades, I have shared my analysis of data on publicly traded companies, breaking down the data into corporate finance and valuation categories. This link, https://people.stern.nyu.edu/adamodar/New_Home_Page/data.html, will take you to the entry page, and you can then either access the most recent data (from the start of 2025, since I update only once a year, for most datasets) or archived data (from previous years). My raw data comes from a variety of sources, and in the interests of not stepping on the toes of my data providers, my data usually reflects industry averages, rather than company-specific data, but it does include regional breakdowns: US, Europe, Emerging Markets (with India and China broken out individually, Australia & Canada & New Zealand) and Japan.  
• Spreadsheets: I am not an Excel ninja, and while my spreadsheet-building skills are adequate, my capacity to make them look polished is limited. I do share the spreadsheets that I use in my classes and work here, with my most-used (by me) spreadsheet being one that I use to value most companies at this link, with a webcast explaining its usage.
• Books: I have written eleven books and co-edited one, spread out across corporate finance, valuation and investing, and you can find them all listed here. Many of these books are in their third or fourth editions, but with each one, you should find a webpage that contains supplementary material for that book or edition (slides, answers to questions at the end of each chapter, data, spreadsheets backing the examples). This is the only section of the spreadsheet where you may encounter a gatekeeper, asking you for a password, and only if you seek access to instructor material. If you are wondering what is behind the gate, it is only the powerpoint slides, with my notes on each slide, but the pdf versions of these slides should be somewhere on the same page, without need for a password.
• Papers: I don't much care much for academic research, but I do like to write about topics that interest or confound me, and you can find these papers at this link. My two most widely downloaded papers are updates I do each year on the equity risk premium (in March) and country risk premiums (in July). Much of the material in these papers has made its way into one or more of my books, and thus, if you find the books unaffordable, you can get that material here for free.
• Blog posts: I will confess that when I write my first blog post on September 17, 2008, I had no idea what a blog was, what I was doing with it, and whether it would last through the following week. In the years since, this blog has become my first go-to, when I have doubts or questions about something, and I am trying to resolve those doubts for myself. In short, my blog has becoming my therapy spot, in times of uncertainty, and I have had no qualms about admitting to these doubts. During 2020, as COVID made us question almost everything we know about markets and the economy, for instance, I posted on where I was in the uncertainty spectrum every week from February 14, 2020 (when the virus became a global problem, not one restricted to China and cruise ships) to November 2020, when the vaccine appeared. You can get all of those posts in one paper, if you click on this link. While my original blog was on Google, in the last two years, I have replicated these posts on Substack (you need to be a subscriber, but it is free) and on LinkedIn. If you are on the latter, you are welcome to follow me, but I have hit my connections limit (I did not even know there was one, until I hit it) and am unable to add connections.
• YouTube: For the last decade, I have posted my class videos on YouTube, grouping them into playlists for each class. You can start with the link to my YouTube channel here, but if you are interested in taking a class, my suggestion is that you click on the playlists and pick on the one that corresponds to the class. Here, for instance, are my links to my Spring 2025 MBA valuation class and my Spring 2025 Corporate Finance class. Starting about a decade, I have also accompanied every one of my blog posts with a YouTube video, that contains the same material, and you can find those posts in its own (very long) playlist. 
• X (Twitter): Some of you have strong feelings about X, with some of those feelings reflecting your political leanings and others driven by the sometimes toxic posting on the platform. I have been a user of the platform since April 2009, and I have used it as a bulletin board, to alert people to content being posted elsewhere. In fact, outside of these "alert" posts, I almost never post on X, and steer away as far away as I can from debates and discussions on the platform, since a version of Gresham's law seems to kick in, where the worst and least informed posters hijack the debate and take it in directions that you do not want it to go.

I cannot think of a single item of content that I have produced in the last decade that is not on one of these platforms, making my professional life an open book, and thus also accessible to any AI entity. The Damodaran bot that I wrote about last year has access to all of this material, and while I signed off on that and one other variant, there are multiple unauthorized versions that have been works-in-progress. 

The Commonalities

    My content has taken many forms including posts, videos, data and spreadsheets, and is on multiple platforms, but there are a few common features that they share:

1. Low tech: I am decidedly low tech, and it shows in my sharing. My website looks like it was designed two decades ago, because it was, and contains none of the bells and whistles that make for a modern website. My blog remains on Google blogger, notwithstanding everything I have been told about how using WordPress would make it more attractive/adaptable, and my posts are neither short nor punchy. Every week, I get people reaching out to me to tell me that my YouTube videos are far too long and verbose, and that I would get more people watching with shorter videos and catchier descriptions, and much as I appreciate their offers to help, I have not taken them up on it., In addition, I shoot almost every one of my videos in my office, sometimes with my dog in the background, and often with ambient noise and mistakes embedded, making them definitely unpolished.  On twitter, I have only recently taken to stringing tweets together and I have never used the long text version that some professional twitter users have mastered. In my defense, I could always claim that I am too old to learn new tricks, but the truth is that I did not start any of my sharing as a means to acquiring a larger social media following, and it may very well be true that keeping my presence low-tech operates as a screener, repelling mismatched users.
2. Process over product: In my writing and teaching, I am often taken to task for not getting to the bottom line (Is the stock cheap or expensive? Should I buy or sell?) quickly, and spending so much time on the why and how, as opposed to the what. Much as my verbosity may frustrate you, it reflects what I think my job is as a teacher, which is to be transparent about process, i.e., explain how I reasoned my way to getting an answer than giving you my answer.
3. Pragmatism over Purity: Though I am often criticized for being an “academic”, I am a terrible one, and if there were an academic fraternity, I would be shunned. I view much of an academic research as navel gazing, and almost everything I write and teach is for practitioners. Consequently, I am quick to adapt and modify models to make them fit both reality and the available data, and make assumptions that would make a purist blanch. 
4. No stock picks or investment advice: In all my years of writing about and valuing markets and individual stocks, I have tried my best to steer away from making stock picks or offering investment advice. That may sound odd, since so much of what I do relates to valuation, and the essence of valuation is that you act on your estimates of value, but here is how I explain the contradiction. I value stocks (like Meta or Nvidia or Amazon or Mercado Libre) and I act (buy or sell) those stocks, based on my valuations, but it is neither my place nor my role to try to get other people to do the same. That said, I will share my story and valuation spreadsheet with you, and if you want to adapt that story/spreadsheet to make it your own, I am at peace with that choice, even if it is different from mine. The essence of good investing is taking ownership of your investment actions, and it is antithetical to that view of the world for me or anyone else to be telling you what to buy or sell.
5. No commercial entanglements: If you do explore my content on any of the platforms it is available on, you will notice that they are free, both in terms of what you pay and how you access them. In fact, none of them are monetized, and if you do see ads on my YouTube videos, it is Google that is collecting the revenue, not me. One reason for this practice is that I am lazy, and monetizing any of these platforms requires jumping through hoops and catering to advertisers that I neither have the time nor the inclination to do. The other is that I believe (though this may be more hope than truth) that one of the reasons that people read what I write or listen to me is because, much as they may disagree with me, I am perceived as (relatively) unbiased. I fear that formalizing a link with any commercial entity (bank, consultant, investor), whether as advisor, consultant or as director, opens the door to the perception of bias. The one exception to the "no commercial entranglements' clause is for my teaching engagements, with the NYU Certificate program and for the handful of valuation seminars I teach in person in the rest of the world. I am grateful that NYU has allowed me to share my class recordings with the world, and I will not begrudge them whatever they make on my certificate classes, though I do offer the same content for free online, on my webpage. I am also indebted to the people and organizations that manage the logistics of my seminars in the rest of the world, and if I can make their life easier by posting about these seminars, I will do so.    

The Imitation Game

    Given that my end game in sharing is to give access to people who want to use my material, I have generally taken a lax view of others borrowing my slides, data, spreadsheets or even webcasts, for their own purposes.

• For the most part, I categorize this borrowing as good neighbor sharing, where just as I would lend a neighbor a key cooking ingredient to save them the trouble of a trip to the grocery store, I am at peace with someone using my material to help in their teaching, save time on a valuation or a corporate finance project, prepare for an interview, or even burnish their credentials. An acknowledgement, when this happens, is much appreciated, but I don't take it personally when none is forthcoming. 
• There are less benign copycat versions of the imitation game - selectively using data from my site to back up arguments, misreading or misinterpreting what I have said and reproducing large portions of my writing without acknowledgement. To be honest, if made aware of these transgressions, I have gently nudged the culprits, but I don't have a legal hammer to follow up.
• The most malignant variations of this game are scams, where the scammers use my content or name to separate people from their money - the education companies that used my YouTube videos and charge for classes, the data sites that copy my data or spreadsheets and sell them to people, and the valuation/investment sites that try to get people to invest money, with my name as a draw. Until now, I have tried, as best as I can, to let people know that they are being victimized, but for the most part, these scams have been so badly designed that they have tended to collapse under the weight of their own contradictions.

It is clear to me that AI is now going to change this game, and that I will have to think about new ways to counter its insidious reach. To get a measure of what the current AI scams that are making the rounds get right and wrong, I did take the time to take a closer look at both the Instagram post and the fake video that are making the rounds. 

• What they get right: The Instagram post, which is in shown below, uses language that clearly is drawn from my posts and an image that is clearly mine.
  
  
  
  Not only does this post reflect the way I write, but it also picked Nvidia and  Palantir as the two firms to highlight,  the first a company that I own and have valued on my blog, and the second a company that I have been talking about as one that I am interested in owning, at the right price, giving it a patina of authenticity. The video looks and sounds like me, which should be no surprise since it had thousands of hours of YouTube videos to use as raw data. Using a yiddish word that I picked up in my days in New York, I have the give the scammers credit for chutzpah, on this front,, but I will take a notch off the grade, for the video's slickness, since my videos have much more of a homemade feel to them.

  

  
  
• What they struggled with most: The scam does mention that Palantir is "overhyped", a word that I use rarely, and while it talks about the company’s valuation, it is cagey about what that value is and there is little of substance to back up the claim. Palantir is a fascinating company, but to value it, you need a story of a data/software firm, with two channels for value creation, one of which looks at the government as a customer (a lower-margin, stickier and lower growth business) and the other at its commercial market (higher margin, more volatile and higher growth). Each of the stories has shades of grey, with the potential for overlap and conflict, but this is not a company where you can extrapolate the past, slap numbers on revenue growth and profitability, and arrive at a value. This post not only does not provide any tangible backing for its words in terms of value, but it does not even try. If these scammers had truly wanted to pull this off, they could have made their AI bot take my class, construct a plausible Palantir story, put it into my valuation spreadsheet and provide it as a link. 
  
  
  
• What they get wrong: To get a sense of what this post gets wrong, you should revisit the earlier part of the post where I talk about my sharing philosophy, and with as much distance as I can muster, here are the false notes in this scam. First, this scam pushes people to join an investment club, where I will presumably guide them on what to buy or sell. Given that my view of clubs is very much that of Groucho Marx, which is that I would not be belong to any club which would admit me as a member, the notion of telling people which stocks to buy cuts against every grain of my being. Second, there is a part of this scam where I purportedly promise investors who decide to partake that they will generate returns of 60% or higher, and as someone who has chronicled that not only do most active investors not keep up with the market, and argued that anyone who promises to deliver substantially more than the market in the long term is either a liar or fraud, this is clearly not me. 
  

In sum, there is good news and bad news in this grading assessment. The good news is that this AI scam gets my language and look right, but it is sloppily done in terms of content and capturing who I am as a person. The bad news is that it if this scammer was less lazy and more willing to put in some work, even with the current state of AI, it would have been easy to bring up the grades on content and message. I will wager that the Damodaran Bot that I mentioned earlier on in this post that is being developed at NYU Stern would have created a post that would have been much more difficult for you to detect as fake, making it a Frankenstein monster perhaps in the making. The worse news is that AI technology is evolving, and it will get better on every one of these fronts at imitating others, and you should prepare yourself for a deluge of investment scams.

An AI Protective Shield

    I did think long about writing this post, wondering whether it would make a difference. After all, if you are a frequent reader of this blog or have read this post all the way down to this point, it is unlikely that you were fooled by the Instagram post or video. It remains an uncomfortable truth that the people most exposed to these scams are the ones who have read little or none of what I have written, and I wish there were a way that I could pass on the following suggestions on how they can protect themselves against the other fakes and scams that will undoubtedly be directed at them. 

1. "Looks & sounds like" not good enough: Having seen the flood of fake AI videos in the news and on social media, I hope that you have concluded that “looks and sounds Iike” is no longer good enough to meet the authenticity test. This remains AI’s strongest suit, especially in the hands of the garden variety scammer, and you should prepare yourself for more fake videos, with political figures, investing luminaries and experts targeted.
2. Steer away from arrogance & hype: I have always been skeptical of the notion that there is “smart” money, composed of investors who know more than the rest of us and are able to beat the market consistently, and for long periods. For the most part, when you see a group of investors (hedge funds, private equity) beating the market, luck is more of a contributor as skill, and success is fleeting. In a talk on the topic, I argued that investors should steer away from arrogance and bombast, and towards humility, when it comes to who they trust with their money, and that applies in spades in the world of AI scams. Since most scammers don’t understand the subtlety of this idea, screening investment sales pitches for outlandish claims alone will eliminate most scams.
3. Do your homework: If you decide to invest with someone, based upon a virtual meet or sales pitch, you should do your homework and that goes well beyond asking for their track records in terms of performance. In my class on investment philosophies, I talk about how great investors through the ages have had very different views of markets and ways of making money, but each one has had an investment philosophy that is unique, consistent and well thought through. It is malpractice to invest with anyone, no matter what their reputation for earning high returns, without understanding that person’s investment philosophy, and this understanding will also give you a template for spotting fakes using that person’s name. 
4. Avoid ROMO & FOMO: In my investing classes, I talk about the damage that ROMO (regret over missing out) and FOMO (fear of missing out) can do to investor psyches and portfolio. 
1. With ROMO (regret over missing out), where you look back in time and regret not buying Facebook at its IPO price in 2012 or selling your bitcoin in  November 2013, when it hit $1000, you expose yourself to two emotions. The first is jealousy, especially at those who did buy Facebook at its IPO or have held on to their bitcoin to see its price hit six digits. The second is that you start buying into conspiracy theories, where you convince yourself that these winners (at least in the rear view mirror) were able to win, because the game was fixed in their favor. Both make you susceptible to chasing after past winners, and easy prey for vendors of conspiracies.
2. With FOMO (fear of missing out), your overwhelming concern is that you will miss the next big multi-bagger, an investment that will increase five or ten fold over the next year or two. The emotion that is triggered is greed, leading you to overreach in your investing, cycling through your investments, as most of them fall short of your unrealistic expectations, and searching for the next “big thing”, making you susceptible to anyone offering a pathway to get there.

Much as we think of scammers as the criminals and the scammed as the victims, the truth is that scams are more akin to tangos, where each side needs the other. The scammer’s techniques work because they trigger the emotions (fear, greed) of the scammed, to respond, and AI will only make this easier to do. Looking to regulators or the government to protection will do little more than offer false comfort, and the best defense is “caveat emptor” or “buyer beware”. 

YouTube Video

Links

Webpage: https://pages.stern.nyu.edu/~adamodar/New_Home_Page/home.htm 

Blog: 

(1) Google: https://aswathdamodaran.blogspot.com 

(2) Substack: https://aswathdamodaran.substack.com 

(3) LinkedIn: https://www.linkedin.com/in/aswathdamodaran/ 

YouTube

https://www.youtube.com/channel/UCLvnJL8htRR1T9cbSccaoVw X: https://x.com/aswathdamodaran?lang=en

Country Risk 2025: The Story behind the Numbers!

    At the start of July, I updated my estimates of equity risk premiums for countries, in an semiannual ritual that goes back almost three decades. As with some of my other data updates, I have mixed feelings about publishing these numbers. On the one hand, I have no qualms about sharing these estimates, which I use when I value companies, because there is no secret sauce or special insight embedded in them. On the other, I worry about people using these premiums in their valuations, without understanding the choices and assumptions that I had to make to get to them. Country risk, in particular, has many components to it, and while you have to ultimately capture them in numbers, I wanted to use this post to draw attention to the many layers of risk that separate countries. I hope, and especially if you are a user of my risk premiums, that you read this post, and if you do have the time and the stomach, a more detailed and much longer update that I write every year.

Country Risk - Dimensions

    When assessing business risk from operating in a country, you will be affected by uncertainty that arises from almost every source, with concerns about political structure (democracies have very different risk profiles than authoritarian regimes), exposure to violence (affecting both costs and revenues),  corruption (which operates an implicit tax) and legal systems (enforcing ownership rights) all playing out in business risk.

I will start with political structure, where the facile answer is that it less risky to operate a business in a democracy than in an authoritarian regime, but where the often unpalatable truth is that each structure brings its own risks. With democracies, the risk is that newly elected governments can revisit, modify or discard policies that a previous government have adopted, requiring businesses to adapt and change to continuous changes in policy. In contrast, an authoritarian government can provide long term policy continuity, with the catch being that changes in the government, though infrequent, can create wrenching policy shifts that businesses have to learn to live with. Keeping the contrast between the continuous risk of operating in a democracy and the discontinuous risk in an authoritarian structure in mind, take a look at this picture of how the world looked in terms of democracy leading into 2025:

Source: Economist Intelligence Unit (EIU)

It is worth noting that there are judgment calls that the Economist made in measuring democracy that you and I might disagree with, but not only is a large proportion of the world under authoritarian rule, but the trend lines on this dimension  also have been towards more authoritarianism in the last decade.    

    On the second dimension, exposure to violence, the effects on business are manifold. In addition to the threat that violence can affect operations, its presence shows up as higher operating costs (providing security for employees and factories) and as insurance costs (if the risks can be insured). To measure exposure to violence, from both internal and external sources, I draw on measures developed and updated by the Institute of  Economics & Peace across countries in 2024:

Institute of Economics & Peace

The Russia-Ukraine war has caused risk to flare up in the surrounding states and the Middle East and central Africa continue to be risk cauldrons, but at least according to the Institute's measures, the parts of the world that are least exposed to violence are in Northern Europe, Australia and Canada. Again, there are judgments that are made in computing these scores that will lead you to disagree with specific country measures (according the Peace Institute, the United States and Brazil have higher exposures to violence than Argentina and Chile, and India has more exposure to violence than China), but the bottom line is that there are significant differences in exposure to violence across the world.

    

    Corruption is a concern for everyone, but for businesses, it manifests in two ways. First, it puts more honest business operators at a disadvantage in a corrupt environment, since they are less willing to break the rules and go along with corrupt practices than their less scrupulous competitors. Second, even for those businesses that are willing to play the corruption game, it creates costs that I would liken to an implicit tax that reduces profits, cash flows and value. The measure of corruption that I use comes from Transparency International, and leading into July 2025, and the heat map below captures corruption scores (with higher scores indicating less corruption), as well as the ten most and least corrupt countries in the world: 

Transparency International

As you can see from the map, there are vast swaths of the world where businesses have to deal with corruption in almost every aspect of business, and while some may attribute this to cultural factors, I have long argued that corruption almost inevitably follows in bureaucratic settings, where you need licenses and approvals for even the most trivial of actions, and the bureaucrats (who make the licensing decisions) are paid a pittance relative to the businesses that they regulate. 

    

    As a final component, I look at legal systems, especially when it comes to enforcing contractual agreements and property rights, central to running successful businesses. Here, I used estimates from the IPRI, a non-profit institution that measures the quality of legal systems around the world. In their latest rankings from 2024, here is how countries measured up in 2024:

Property Rights Alliance

In making these assessments, you have to consider not just the laws in place but also the timeliness with which these laws get enforced, since a legal system where justice is delayed for years or even decades is almost as bad as one that is capricious and biased. 

Country Risk - Measures

    The simplest and most longstanding measure of country risk takes the form of sovereign ratings, with the same agencies that rate companies (S&P, Moody's and Fitch) also rating countries, with the ratings ranging from Aaa (safest) to D (in default). The number of countries with sovereign ratings available on them has surged in the last few decades; Moody’s rated 13 countries in 1985, but that number increased to 143 in 2025, with the figure below listing the number of rated countries over time:

Note that that the number of Aaa rated countries stayed at eleven, even while more countries were rated, and has dropped from fifteen just a decade ago, with the UK and France losing their Aaa ratings during that period. In May 2025, Moody's downgraded the United States, bringing them in line with the other ratings agencies; S&P downgraded the US in 2011 and Fitch in 2023. The heat map below captures sovereign ratings across the world in July 2025:

Moody's

While sovereign ratings are useful risk measures, they do come with caveats. First, their focus on default risk can lead them to be misleading measures of overall country risk, especially in countries that have political risk issues but not much default risk; the Middle East, for instance, has high sovereign ratings. Second, the ratings agencies have blind spots, and some have critiqued these agencies for overrating European countries and underrating Asian, African and Latin American countries. Third, ratings agencies are often slow to react to events on the ground, and ratings changes, when they do occur, often lag changes in default risk.

    If you are leery about trusting ratings agencies, I understand your distrust, and there is an alternative measure of sovereign default risk, at least for about half of all countries, and that is the sovereign credit default swap (CDS) market, which investors can buy protection against country default. These market-determined numbers will reflect events on the ground almost instantaneously, albeit with more volatility than ratings. At the end of June 2025, there were about 80 countries with sovereign CDS available on them, and the figure below captures the values:

The sovereign CDS spreads are more timely, but as with all market-set numbers, they are subject to mood and momentum swings, and I find using them in conjunction with ratings gives me a better sense of sovereign default risk.

    If default risk seems like to provide too narrow a focus on countr risk, you can consider using country risk scores, which at least in principle, incorporate other components of country risk. There are many services that estimate country risk scores, including the Economist and the World Bank, but I have long used Political Risk Services (PRS) for my scores.. The PRS country risk scores go from low to high, with the low scores indicative of more country risk, and the table below captures the world (at least according to PRS):

Political Risk Services (PRS)

There are some puzzling numbers here,  with the United States coming in as riskier than Vietnam and Libya, but that is one reason why country risk scores have never acquired traction. They vary across services, often reflecting judgments and choices made by each service, and there is no easy way to convert these scores into usable numbers in business and valuation or compare them across services.

    

Country Risk - Equity Risk Premiums

    My interest in country risk stems almost entirely from my work in corporate finance and valuation, since this risk finds its way into the costs of equity and capital that are critical ingredients in both disciplines. To estimate the cost of equity for an investment in a risky country. I will not claim that the approaches I use to compute equity risk premiums for countries are either original or brilliant, but they do have the benefit of consistency, since I have used them every year (with an update at the start of the year and mid-year) since the 1990s. 

    The process starts with my estimate of the implied equity risk premium for the S&P 500, and I make this choice not for parochial reasons but because getting the raw data that you need for the implied equity risk premium is easiest to get for the S&P 500, the most widely tracked index in the world. In particular, the process requires data on dividends and stock buybacks on the stocks in the index, as well as expected growth in these cash flows over time, and involves finding the discount rate (internal rate of return) that makes the present value of cash flows equal to the level of the index. On June 30, 2025, this assessment generated an expected return of 8.45% for the index:

Download ERP spreadsheet

Until May 2025, I just subtracted the US 10-year treasury bond rate from this expected return, to get to an implied equity risk premium for the index, with the rationale that the US T.Bond rate is the riskfree rate in US dollars. The Moody’s downgrade of the US from Aaa to Aa1 has thrown a wrench into the process, since it implies that the T.Bond rate has some default risk associated with it, and thus incorporates a default spread. To remove that risk, I net out the default spread associated with Aa1 rating from the treasury rate to arrive at a riskfree rate in dollars and an equity risk premium based on that:

Riskfree rate in US dollars       = T.Bond rate minus Default Spread for Aa1 rating

                                                            = 4.24% - 0.27% = 3.97%

Implied equity risk premium for US = Expected return on S&P 500 minus US $ riskfree rate

                                                            = 8.45% - 3.97% = 4.48%

Note that this approach to estimating equity risk premiums is model agnostic and reflects what investors are demanding in the market, rather than making a judgment on whether the premium is right or what it should be (which I leave to market timers).

       To get the equity risk premiums for other countries, I need a base premium for a mature market, i.e., one that has no additional country risk, and here again, the US downgrade has thrown a twist into the process. Rather than use the US equity risk premium as my estimate of the mature market premium, my practice in every update through the start of 2025, I adjusted that premium (4.48%) down to take out the US default spread (0.27%), to arrive at the mature market premium of 4.21%. That then becomes the equity risk premium for the eleven countries that continue to have Aaa ratings, but for all other countries, I estimate default spreads based upon their sovereign ratings. As a final adjustment, I scale these default spreads upwards to incorporate the higher risk of equities, and these become the country risk premiums, which when added to the mature market premium, yields equity risk premiums by country. The process is described below:

The results from following this process are captured in the picture below, where I create both a heat map based on the equity risk premiums, and report on the ratings, country risk premiums and equity risk premiums, by country:

Download equity risk premium, by country

If you compare the equity risk premium heat map with the heat maps on the other dimensions of country risk (political and legal structures, exposure to violence and corruption), you will notice the congruence. The parts of the world that are most exposed to corruption and violence, and have capricious legal systems, tend to have higher equity risk premiums. The effects of the US ratings downgrade also manifest in the table, with the US now having a higher equity risk premium than its Aaa counterparts in Northern Europe, Australia and Canada.

A User's Guide 

    My estimates of equity risk premiums, by country, are available for download, and I am flattered that there are analysts that have found use for these number. One reason may be that they are free, but I do have concerns sometimes that they are misused, and the fault is mine for not clarifying how they should be used. In this section, I will lay out steps in using these equity risk premiums in corporate finance and valuation practice, and  if I have still left areas of  grey, please let me know.

Step 1: Start with an understanding of what the equity risk premium measures

    The starting point for most finance classes is with the recognition that investors are collectively risk averse, and will demand higher expected returns on investments with more risk. The equity risk premium is a measure of the “extra” return that investors need to make, over and above the riskfree rate, to compensate for the higher risk that they are exposed to, on equities collectively. In the context of country risk, it implies that investments in riskier countries will need to earn higher returns to beat benchmarks than in safer countries. Using the numbers from July 2025, this would imply that investors need to earn 7.46% more than the riskfree rate to invest in an average-risk investment in India, and 10.87% more than the riskfree rate to invest in an average risk investment in Turkey.

    It is also worth recognizing how equity risk premiums play out investing and valuation. Increasing the equity risk premium will raise the rate of return you need to make on an investment, and by doing so, reduce its value. That is why equity risk premiums and stock prices move inversely, with the ERP rising as stock prices drop (all other thins being held constant) and falling as stock prices increase. 

Step 2: Pick your currency of analysis (and estimate a riskfree rate)

    I start my discussions of currency in valuation by positing that currency is a choice, and that not only can you assess any project or value any company in any currency, but also that your assessment of project worth or company value should not be affected by that choice. Defining the equity risk premium as the extra return that investors need to make, over and above the risk free rate, may leave you puzzled about what riskfree rate to use, and while the easy answer is that it should be the riskfree rate in the currency you chose to do the analysis in, it is worth emphasizing that this riskfree rate is not always the government bond rate, and especially so, if the government does not have Aaa rating and faces default risk. In that case, you will need to adjust the government bond rate (just as I did with the US dollar) for the default spread, to prevent double counting risk.  

Staying with the example of an Indian investment, the expected return on an average-risk investment in Indian rupees would be computed as follows:

Indian government bond rate on July 1, 2025 = 6.32%

Default spread for India, based on rating on July 1, 2025 = 2.16%

Indian rupee risk free rate on July 1, 2025 = 6.32% - 2.16% = 4.16%

ERP for India on July 1, 2025 = 7.46%

Expected return on average Indian equity in rupees on July 1, 2025 = 4.16% + 7..46% = 11.62%

Note also that if using the Indian government bond rate as the riskfree rate in rupees, you would effectively be double counting Indian country risk, once in the government bond rate and once again in the equity risk premium.

    I know that the ERP is in dollar terms, and adding it to a rupee riskfree rate may seem inconsistent, but it will work well for riskfree rates that are reasonably close to the US dollar risk free rate. For currencies, like the Brazilian real or Turkish lira, it is more prudent to do your calculations entirely in US dollars, and convert using the differential inflation rate:

US dollar riskfree rate on July 1, 2025 = 3.97%

ERP for Turkey on July 1, 2025 = 10.87%

Expected return on average Turkish equity in US $ on July 1, 2025 = 3.97% + 10.87% = 14.84%

Expected inflation rate in US dollars = 2.5%; Expected inflation rate in Turkish lira = 20%

Expected return on average Turkish equity Turkish lira on July 1, 2025 = 1.1484 *(1.20/1.025) -1 = 34.45%

Note that this process scales up the equity risk premium to a higher number for high-inflation currencies.

Step 3: Estimate the equity risk premium or premiums that come into play based on operations

   Many analysts use the equity risk premiums for a country when valuing companies that are incorporated in that country, but I think that is too narrow a perspective. In my view, the exposure to country risk comes from where a company operates, not where it is incorporated, opening the door for bringing in country risk from emerging markets into the cost of equity for multinationals that may be incorporated in mature markets. I use revenue weights, based on geography, for most companies, but I am open to using production weights, for natural resource companies, and even a mix of the two. 

In corporate finance, where you need equity risk premiums to estimate costs of equity and capital in project assessment, the location of the project will determine which country’s equity risk premiums come into play. When Amazon decides to invest in a Brazilian online retail project, it is the equity risk premium for Brazil that should be incorporated, with the choice of currency for analysis determining the riskfree rate. 

Step 4: Estimate project-specific or company-specific risk measures and costs

    The riskfree rate and equity-risk premiums are market-wide numbers, driven by macro forces. To complete this process, you need two company-specific numbers:

• Not all companies or projects are average risk, for equity investors in them, and for companies that are riskier or safer than average, you need a measure of this relative risk. At the risk of provoking those who may be triggered by portfolio theory or the CAPM, the beta is one such measure, but as I have argued elsewhere, I am completely at home with alternative measures of relative equity risk. The cost of equity is calculated as follows: 

Cost of equity = Riskfree rate + Beta × Equity Risk Premium

The beta (relative risk measure) measures the risk of the business that the company/project is in, and for a diversified investor, captures only risk that cannot be diversified away. While we are often taught to use regressions against market indices to get these betas, using industry-average or bottom-up betas yields much better estimates for projects and companies.

• For the cost of debt, you need to estimate the default spread that the company will face. If the company has a bond rating, you can use this rating to estimate the default spread, and if it is not, you can use the company's financials to assess a synthetic rating.

Cost of debt =Riskfree Rate + Default spread

Harking back to the discussion of riskfree rates, a company in a country with sovereign default risk will often bear a double burden, carrying default spreads for both itself and the country.

The currency choice made in step two will hold, with the riskfree rate in both the cost of equity and debt being the long-term default free rate in that currency (and not always the government bond rate).

Step 5: Ensure that your cash flows are currency consistent 

    The currency choice made in step 2 determines not only the discount rates that you will be using but also the expected cash flows, with expected inflation driving both inputs. Thus, if you analyze a Turkish project in lira, where the expected inflation rate is 20%, you should expect to see costs of equity and capital that exceed 25%, but you should also see growth rates in the cash flows to be inflated the same expected inflation. If you assess the same project in Euros, where the expected inflation is 2%, you should expect to see much lower discount rates, high county risk notwithstanding, but the expected growth in cash flows will also be muted, because of the low inflation.

    There is nothing in this process that is original or path-breaking, but it does yield a systematic and consistent process for estimating discount rates, the D in DCF. It works for me, because I am a pragmatist, with a valuation mission to complete, but you should feel free to adapt and modify it to meet your concerns. 

YouTube Video

Paper

1. Country Risk Determinants: Determinants, Measures and Implications - The 2025 Edition

Datasets

1. Equity Risk Premiums, by country - July 2025

Country Risk Links

1. EIU Democracy Index
2. Global Peace Index (Exposure to Violence)
3. Corruption Index
4. International Property Rights Index
5. Moody's Sovereign Ratings
6. Political Risk Services (PRS) Country Risk Scores

Spreadsheets

1. Implied Equity Risk Premium for S&P 500 on July 1, 2025