The Situational Awareness Blow-up: The Collateral Damage from Investing Conviction!

     Earlier this year, I was asked what I thought about Leopold Aschenbrenner and I admitted that I knew little about him other than what I had read about him, more in social media, than in the press - that he was a 25-year-old wunderkind who had started at OpenAI but had left to start a hedge fund. That hedge fund, built entirely around a bet that AI would pay off big time and near term, buying companies in the AI orbit and selling short on the businesses (especially software) that AI would disrupt, had been able to raise billions of dollars from well-heeled and presumably sophisticated investors, and had  posted eye-popping returns, up almost 450% through late June. Success of that magnitude needs no nitpicking, but it is worth remembering that there is nothing that markets enjoy more than cutting inflated egos and reputations down to size. In this case, the fall from grace was precipitous, and over the course of four weeks, the fund's public equity holdings lost more than two thirds of its value, but was also forced to liquidate, with Citadel buying almost all of its public equity holdings.

    The reads on the swift rise and fall of Leo have been fascinating, a Rorschach test of investing priors. For older investors, the lesson was that you can be blessed with intelligence, but that wisdom required experience, which, at least in their saying, conveniently comes with age. For value investors, many of whom chafed at Leo being hailed as the next Buffett, there was vindication that there will never be another Buffett. For AI skeptics, who have long been on the lookout for catalysts that will break AI fever, there was at least a brief moment of hope that this was the catalyst. There is some truth and some overreach in each of these responses, and I don't plan to rehash them. Instead, I would like to use this story to talk about investing conviction, words used mostly in a favorable way, when people talk about success in markets. I am not as convinced that investing conviction is a net plus, but to get to that conclusion, I think we need to look at what it is, where it comes from and what it leads investors to do. 

The Story of Situational Awareness

    The Situational Awareness story is inextricably tied to the story of Leopold (Leo) Aschenbrenner. The short version of his life story is that he was born in Germany in 2001, and enrolled at Columbia University when he was 15. After graduating with a degree in economics in 2021, doing research briefly at Oxford University and working at Sam Bankman-Fried's FTX fund, Leo joined OpenAI as part of the team working on AI safety. He was fired in 2024 for leaking data on the firm, though his motivations for doing so are murky and he contests the allegation, and he published a long (167 page) paper titled "Situational Awareness: The Decade Ahead", which was not only widely circulated, but also became the blueprint for the fund that he created.

    The Situational Awareness fund, founded in July 2024, and initially funded by tech luminaries, quickly took off as its bets on AI chips and infrastructure and against AI-damaged sectors paid off. Its early success allowed it to attract more money, with Jane Street being one of the more prominent names involved., and with the additional capital in play, it expanded its presence. While most of the companies that made the fund's list were publicly traded, it also had a stake that Leo had acquired in Anthropic, still a private business. The numbers posted by the fund made investors notice, as can be seen by its rise From August 7, 2025, to June 23, 2026, its highest mark day:

Source: Portfolios Lab

Measuring returns from August 7, 2025, Situational Awareness was up about 367% through June 19, 2026 and its returns since founding are even more stratospheric. Even at its peak, there were three caveats that any investor with experience in the market would (or should) have brought up. First, in market time, where decades of over performance are needed to separate luck than skill, a fund that has been successful for a little more than two years qualifies more as a shooting star than as a beacon of light. Second, to deliver returns of this magnitude, you not only have to be right in your guesses, but those guesses must be super-charged by adding substantial leverage to your strategy, either explicitly (by borrowing) or implicitly (by using options). Third, the fund followed the classic 2% (of funds under management) and 20 (% of specified upside) fee structure, an abomination that not only creates an almost insurmountable handicap, in the long term, for investors in the fund, but also encourages reckless risk taking on the part of management.

    If the rise of the fund was breathtaking, its fall was even more so, and you can see the meltdown in the four weeks of July in the graph below, which looks at the fund performance from June 19, 2026 to July 29, 2026, the last day of trading for the day, before the fund was liquidated:

Source: Portfolios Lab

Note that the loss of principal (of more than 43%), with the Anthropic holding value retaining mostly intact, as a private holding, but with the public investment portion of the portfolio down by almost 67%. I am sure that there will be case studies and forensic analysis of what happened in these weeks, but for me, the lesson is one of market symmetry. What the market gives easily, it also takes away just as easily, and if you put into place strategies that are designed to deliver outsized returns, you have to live with the reality that you can have outsized losses. The surprise, though, for many is not that the fund lost money in July, but that it did not survive the month, and was forced to sell much of its public investment portfolio to Citadel, at prices, that at least in hindsight, look like bargain basement levels. 

Conviction: What is it and where does it come from?

    How do I get from the Situational Awareness story to a discussion of investment convictions? Simple! Leo's core belief that AI would win the battle with the status quo in most businesses, and that the win would be decisive and quick, was not unique, and not only are there other investors who shared that view, but there are also companies that are investing in AI cap ex, driven by that view. That said, to get from that view to a hedge fund built entirely around buying AI winners and selling AI losers requires that the belief to be deeply set and using debt to magnify those returns suggests strong conviction.

What is investment conviction?

    Before we embark on a discussion of investment conviction, it is worthwhile to start with an understanding of what it means. While there a myriad of definitions out there, the general consensus is that investment conviction measures the belief that an investment opportunity will generate significant returns, relative its risks, and with that definition, you can see that conviction is a continuum, rather than an absolute. 

At one end of the spectrum, you have absolute conviction, where you know (or think you know) with certainty that an investment will pay off. At the other end of the spectrum is investment mush, where your feelings about an investment paying off are so weak that you are unwilling to even voice that opinion, let alone put money behind it. With most investments, you fall in the middle, with the variations being in the degree of confidence that you have in being right.

    As you can see, the elevation of conviction as something to be sought after in investing is because in the complete absence of conviction, you will not act and that paralysis can result in portfolios entirely or almost entirely held as cash. I don't think, though, that even the strongest proponents of conviction as a good quality in investing  would make the argument that you should feel certain about the outcome of investments, when uncertainty is part and parcel of investing. 

Where does conviction come from?

    So, what is it that determines investment conviction or the lack of it? To generate a basis for that discussion, let's go back to basics, and start with an assessment of what has to happen for an investment to be viewed as a money maker. No matter what your investment philosophy, the process starts with a market price for an investment, and an assessment of what you believe is a "fair price" for that same investment. I am being agnostic about how you arrive at the fair price, leaving the door open for chartists, who may find it by looking at past price patterns, fundamentalists, who believe that you can assess fair price, only by looking at the fundamentals of the investment and traders, who may be in possession of information that leads them to believe that the current price is wrong. For the process to deliver winnings, though, there is a second part to the process that often gets less attention, which is that the market has to correct, with the market price moving towards or even to your fair price. 

To have conviction in an investment, you therefore need to believe strongly in three aspects of this process:

1. That your assessment of "fair" price is right (or at least more right than the market consensus) 
2. That the market will correct, i.e., that the market price will move to, or towards, your fair price
3. That this correction will happen during the time you plan to hold the investment (your time horizon), either because the investment has an expiration date (maturity) or because you feel that there will be a catalyst that causes the correction.

With this description in place, you can see that investment conviction will depend on the investment in question, the market that it is priced in and on the investor making the judgment.

a. Investment Mispricing

    If the investment process starts with an assessment of a fair price that is different from the market price, there are at least four reasons why you may be more confident in your assessment of the price of an investment, relative to the market consensus:

1. Private information: At the risk of treading on or crossing the line between the legal and the illegal, you may be in possession of information about an investment that is not available (or at least widely enough available to the public to be priced in) that you believe will change its price.
2. Information processing: To the extent that private information is rarely available to investors, and even if available, difficult to act on legally, much of active investing is built around collecting and processing information that is available to the market. That private information can range from past prices and trading volume (used by technical analysts) to public filings (the financial data that the company provides, often the basis for fundamental investors) to quasi-public information (analyst forecasts and revisions, hedge fund and mutual fund holdings). If you are using this information to assess a fair price, it is because you believe that you have found patterns in the data that others have not.
3. Business understanding: In some cases, your fair price will derive from your belief that you understand the business economics for a company better than other public investors do, with your superior understanding coming either from working in the business or from technical training. This is especially true in complex businesses (like bio pharma) and complicated assets, and likely to be  more the case with young companies, where financial history can stand in for business understanding.
4. Pricing mistake: There are some investment theses that start with a market mistake, whether it be in pricing an individual asset or a pair of assets. With a pair of assets, often with similar fundamentals, the mispricing manifests with one of the assets being mispriced against the other, and the correction take place when the mispricing disappears. It is at the heart of derivatives trading, where options or futures on a traded asset can be mispriced enough that you can lock in the profits from the pricing mistake, with the guarantee of correction, when the option or futures expire.

In sum, you can see that the degree of investment conviction that an investor has can vary across different asset markets (real estate, equities, fixed income, derivatives), geographies (developed versus emerging markets) and within equities, across industry groups and sectors (less for technology and more for utilities, for example).

b. Market correction

    Investment conviction may start with the spotting of a market pricing mistake, but for conviction to build, you need to get a measure of when, why and how the market will correct its mistaket. Here are some of the forces that can cause variations on the market correction dimension:    

1. Finite maturity versus indeterminate end game: An investment with a finite maturity date comes with a greater likelihood that prices will correct than one without. A bond that is mispriced, by itself, or against other bonds of equal maturity, comes with a greater chance of correction than a stock that is mispriced against its own fundamentals or a paired stock. For the same reasons, if you can lock in a mispriced option against its underlying asset (or stock) or against other options of equal maturity, you are moving the odds in favor of correction. It should come as no surprise that true arbitrage, i.e., positions that can lock in guaranteed profits that exceed the riskfree rate are almost always in the fixed income or derivatives markets and that much of what passes for arbitrage in equities is quasi or pseudo arbitrage, where risk remains in the position.
2. Market frictions: Market mispricing can sometimes reflect market inattention or irrational trading, but they more often are the consequence of market frictions, including restrictions on selling short and exerting control over an investment (such as buying and liquidating a company). If that is the case, it is possible that market mistakes, while visible and seeming obvious to everyone involved, may never get corrected, or at least not get corrected until the friction is removed.
3. Market liquidity and depth: In equity markets, where there is no date by which mispricing has to disappear, corrections often require catalysts, i.e., events that lead market participants to reassess a market price, and to correct it. Those catalysts can come from corporate disclosures (earnings reports, for instance) by the mispriced company, corporate restructuring (divestitures and spin offs) or high profile investors (activists taking a stake in a under priced company or selling short an overpriced one). All of these catalysts are more likely to be present in liquid and deep markets, where information flows are more frequent and varied.
4. Investment time horizon: No matter what the market mistake, it can be argued that  having time as an ally, and being able to wait for longer periods, for market corrections, should not only give you a greater chance of gaining from a market correction, but also give you more conviction in your investment, other things being equal.

The bottom line is that you can feel (relatively) certain that an investment is mispriced, but have no conviction in that investment, if you have no sense or faith that the market will correct in your time frame for investing. 

c. Investor characteristics

    Is it possible for two investors to find the same market mistake, with the same underlying rationale for the mispricing, and have different degrees of conviction in following through? Absolutely, and while part of the reason is differing time horizons, there are other investor-specific forces that also come into play:

• Intelligence and educational background: This may be a generalization, and if you disagree, you should take exception, but the smarter an investor is, and the more exceptional his or her educational background (the right schools, credentials and certification), the greater the conviction that investor is likely to bring to investments. One reason is that it becomes easier to attribute perceived market mistakes to the lack of intelligence of other market participants than it is to take a closer look at real reasons why they may not be mistakes in the first place.
• Personality: Like conviction, self confidence falls on a spectrum with wide differences across human beings. Some investors measure low on the self confidence scale, and look to others for big decisions. Others measure higher  on the self-confidence scale and are willing to make decisions with incomplete information and in the face of uncertainty and disagreement. Still others have so much self confidence and so little self doubt that they risk having convictions that are out of sync with reality.  Over five decades of research in behavioral finance has highlighted overconfidence as one of the key drivers of irrational investor behavior, and underscored the reality that not only does this trait vary widely among individuals, but also that the most overconfident players often rise to the top of the investment and corporate world. In the conviction discussion, overconfidence enters the game early and is perhaps the best explainer of why some investors, looking at what they think is a market mistake, feel so much more convinced that they are right than other investors looking at exactly the same mistake.
• Track record: When you invest, you receive almost instantaneous and continuous feedback on whether your investments are paying off, and while investment success is always preferable to failure, it is undeniable that some of the worst investment lessons are learned from that success. Much as Wall Street likes its adages of "not mistaking dumb luck for skill", investors are quick to forget them when an investment bet that they make pays off, especially when their success leads to iconic and admiring profiles ("the next Buffett") and investor capital pouring in.

You will notice that I don't list age as an additional characteristic, but that is because I don't equate aging with wisdom or temperance. It is true that aging usually muddies your investment track record, and that there is no better bound on over confidence than losing lots of money on what you thought was a sure bet. 

The Consequences of Conviction

    At this point in the post, I don't blame you for wondering why conviction is such a big deal, since buying Palantir or SpaceX with low conviction counts just as much buying shares in these companies with high conviction. Conviction matters in investing because it affects two choices that investors make - the sizing of positions (with more conviction leading to larger positions in the same investment) and the use of financial leverage (with more conviction often translating into a willingness to borrow more money to fund the investment). 

Concentration

    One of the fundamental questions in investing, and one that evokes strong disagreement, is how much, if at all, investors should spread their bets. The debate is an old one and there are many views that fall between two extremes. At one end is the advice that you get from a believer in efficient markets: be maximally diversified, across asset classes, and within each asset class, across as many assets you can hold. The proverbial “market portfolio” includes every traded asset in the market, held in proportion to its market value. At the other is the “go all in” investor, who believes that if you find a significantly undervalued company, you should put all or most of your money in that company, rather than dilute your upside potential by spreading your bets. The discussion of investment conviction ties into the answer to this question. 

I am not an absolutist on this front, because what you do as an investor should reflect your circumstances.  At one limit, if you are certain about your assessment of value for an asset and that the market price will adjust to that value within your time horizon, you should put all of your money in that investment.  This may seem like an impossible dream, but it it is what you hope to pull off if you find mispricing in the bond or derivatives markets (true arbitrage), where you can lock in the mispricing, with a guaranteed price correction at maturity (of the bond or options).  At the other limit, if you have doubts aplenty and no conviction in your investment choices, you should be as diversified as you can get, given transactions costs. If you have no transactions costs, you should own a little piece of everything, a very real choice in a world of index funds and ETFs. After all, you gain nothing by holding back on diversification and your portfolio will be deliver less return per unit of risk taken. If you are active investor who is constantly in this position (of having no conviction), it is best to retire the "active" part of your investment profile and go all in on index funds. 

    For most active investors then, the question of how much to diversify will depend in large part on how strong or weak their investment conviction is, and that, in turn, depends on what investments these investors are focused upon. In this context, I would argue that the right amount of conviction and by extension concentration (or diversification) will depend upon the types of companies you invest in (with more diversification needed when you invest in younger companies) and your time horizon (with concentration increasing with time horizon) 

Leverage

    Financial leverage is an instrument that investors can use to enhance returns, but its use in investing has always been controversial. While borrowing money to fund an investment will increase upside, if you are right, it will also magnify downside, and in some cases, precipitate collapse, if you are wrong. In the early year of investing, financial leverage generally took the form of borrowing money to buy riskier investments (stocks), but the wave of default and distress triggered for investors by the great depression led to restrictions on the use of margin in stock markets. However, those restrictions varied across investor groups (with individuals facing more restrictions than institutions) and across asset classes (with more leverage in real estate than in stocks). The growth of derivatives markets opened the door to bypassing these borrowing restrictions, since buying a (naked) call option is equivalent to borrowing the underlying asset with debt, with leverage increasing with how out of the money the call option is.

    Since leverage magnifies both upside and downside, it stands to reason that investors with more conviction in their investment, i.e., that it is under priced and that correction is imminent or very likely to happen, will borrow more money than investors with less conviction:

When you are certain that an investment will pay off, you can use maximal leverage, and in true arbitrage, this leverage can be used to convert small mispricing into pure profits. In fact, when options and futures are mispriced relative to their underlying assets, you can borrow 100% of your investment needs, and since the pricing error will disappear at maturity, make a pure profit.

    In sum, financial leverage magnifies your core investment judgments. If they are good, leverage will make them look better over time, and if they are bad, they will make them worse. That said, there is a component to the use of leverage that needs to be brought into the picture. Even if you are a good investor, with solid conviction in your investment ideas, using debt to turbocharge your returns can sometimes shrink your time horizon by forcing you to liquidate your mispriced investments before the market corrects its mistakes. That truncation risk eliminates the possibility that you can come back from your losses, and perhaps even have your investment thesis vindicated.

A Corporate Life Cycle Perspective on Conviction, Concentration and Leverage

    I use the corporate life cycle construct in almost every aspect of finance, because it not only helps understand corporate and investor behavior, but also provides perspective on why one size (or proposal) does not fit all. The corporate life cycle maps out a firm's evolution from a start-up to a growth company to maturity and eventual decline, and traces out changes in revenues, earnings and risk over the aging process:

There is no deep intellectual insight here, but as companies age, the challenges that investors face in pricing them shifts. With young firms, where there is little history, the business model is still in flux and the value lies almost entirely in the future, the pricing will be less precise than for more mature firms, where there is an established business model and more financial history, and more of the value comes from investments already made. For declining firms, where liquidation looms as a viable option, the pricing exercise becomes one of estimating liquidation value, i.e., what others will pay for the individual assets owned by the firm. The higher pricing uncertainty that investors face when valuing younger companies is accompanied by a second problem, which is that pricing mistakes, if they exist, need catalysts for market correction. Those catalysts are less frequent and decisive with younger companies, where earnings reports have little of substance to report and operating targets are diffuse, creating more open questions about whether a market mistake will be corrected, and if so, when that will happen.

    Using this framework, you can see that you can be more tolerant of concentrated portfolios, with debt added on, when you invest in mature companies than you should be when investing in younger companies. By the same token, investors who find pricing mistakes in younger companies, but are daunted by the noisiness of their estimates or uncertainty about markets correcting, and thus are low on the conviction scale, can overcome their reluctance to act by spreading their bets across many such companies. As some of you may be aware, I did value SpaceX at the time of its IPO at about $100 a share, and as the price has drifted down towards that price, I may very well be faced with an underpriced stock (where the market price drops below $100). Given the uncertainty that is associated with my estimate, and you can see it in the simulation that I reported in my post, it is unlikely that I would ever have sufficient conviction to make SpaceX the biggest or only investment in my portfolio, but I stand ready to buy the stock as part of a portfolio, where it is one of many bets that I make on markets.

Lessons from Leo

I started this post with the story of Situational Awareness and Leo Aschenbrenner, but I spent most of it talking about investment conviction, what it is, its sources and consequences. I want to end the post by returning to Leo’s story and what we can learn from it as investors.

Lesson 1: Investment actions that are inconsistent with investment conviction risk ruin

    I have no issues with Leo's story of AI winning big in the near-term and buying the winners and selling the losers that will result. It is macro story investing, and it has worked for some in the past and failed for others, but if timed right, it can deliver significant returns.  In fact, I will concede that Leo knows far more about AI than I ever will and is using that knowledge in constructing his AI story. I also have no bone to pick with investors using financial leverage to supercharge their returns,  with low-risk investments, though I remain concerned that the (2 & 20) fee structure may lead them to use too much debt. My concern with Situational Awareness, as a fund, and this would have been true on June 19, even at it peak, is that combining a macro story about AI winning with maximal leverage creates a time bomb. The AI story, no matter how well told, has multiple obstacles to overcome, some related to business economics and some to politics and regulation, and is a risky bet, and it makes little sense to fund it with significant amounts of debt. I know that there are defenders who will point to the fact that the fund, even after its markdown, was up substantially from its inception date, but the fact that leverage cut the fund's life short only strengthens the case that if it had been run with less debt, it would have had a bad month in July, but lived to tell the tale and perhaps even deliver on its AI promise.

Lesson 2: Momentum is a wild card in every investment strategy, and you ignore it at your own peril.

It is a well-established finding in equity markets that momentum is one of the strongest forces moving markets and that it can overwhelm the best planned strategies of most investors. If you look at the composition of Situation Awareness portfolio through much of its rise and fall., the long positions were primarily in companies that benefit from the build-up of Ai architecture, selling their products and services to the hyper scalers and LLM companies, and the short positions were in software and other businesses that would be disrupted by the rise of AI. With both groups, Situational Awareness was taking bets that were in line with what the market was pricing in already, albeit in a more concentrated and leveraged form. While market observers were quick to link both the rise and fall of Situational Awareness to the AI story, you can make just as strong a case that much of that happened at the fund over its brief existence can be explained by momentum, with leverage acting as a super charger; continued momentum generated the outsized return though June 19, and the market reversal in July caused the correction.

Lesson 3: Humble money beats smart money

    The legend of smart money persists in markets, where investors who are smarter than the rest of us, with access to information and capital that others do not possess, deliver supersize returns for themselves and those that they invite into their inner circle. That legend serves everyone's interests, since the smart money uses its reputation to attract more capital and the not-so-smart money has someone else (hedge funds, insiders, activists) to blame for investment setbacks. While many money managers aspire to be part of the smart money group, most never make it into that rarefied circle, and those that do often have to pay their dues over long periods. Leo Aschenbrenner, in contrast, broke into the group in just a few months, perhaps helped by his pedigree as an AI insider and with an assist from his post on the coming AI revolution. The problem with smart money is that  its self-regard makes its susceptible to attributing more precision to its own convictions, than merited by the circumstances, and that, in turn,  results in over reach (portfolios that are much too concentrated or levered). Situational Awareness clearly overused leverage, and while some will attribute that to the youth and inexperience of its lead manager, it is worth remembering Long Term Capital Management, where John Merriweather, after a long and distinguished trading tenure at Salomon Brothers, aided by two Nobel Prize winners in economics, brought the fund to its knees by borrowing too much on risky trades. In a post from years ago, I drew a contrast between smart money and humble money, with the former including investors who attribute every basis point of excess return earned to their investing brilliance, and the latter open about the fact that their performance, no matter how stellar, has as much to do with being in the right place at the right time (being lucky) as it has to do with skill. Investors looking for someone to manage their money are likely to do much better with the latter than the former.   

    I hope that you don't view this as a hit piece on Leo or AI, since that was not my intent. In fact, I hope that Leo persists and perhaps even comes back as a fund manager, since he strikes me as an original thinker who is willing to take a stand, both scarce qualities among active fund managers.  I also hope that he has learned some lessons, especially on humility and restraint, for his next go around, and that he adopts a fee structure that gives his investors a chance of beating the market in the long term.

YouTube Video

Links to the Leo Aschenbrenner story

1. Situational Awareness: The Decade Ahead (Leo's post on AI)
2. The 24-year old AI Wiz who counts Jane Street as an investor (The Wall Street Journal in early June, prior to blow-up)
3. Inside the Meltdown of a Wunderkind's AI Hedge Fund (New York Times)
4. His Wedding Guests were arriving - Just as his $45 billion fund was falling apart (The Wall Street Journal)
5. What does the humbling of Leopold Aschenbrenner mean for the AI bubble? (The NewYorker)

Information Timing and Release: The Gaming of Guidance!

    I am a lapsed academic, insofar as I have not submitted a paper for publication in more than two decades, but to acquire academic status, I did have to earn a PhD in the distant past. My doctoral dissertation, which like most doctoral theses is little noted and long forgotten, was completed in 1984 and focused on how the frequency of and delays in the release of information plays out in stock price volatility, skew and jumps. I don't plan to rehash that paper, but there have been two long running news stories that reminded me of it. The first is a proposal being floated by the Securities Exchange Commission (SEC) to replace quarterly reporting of financial statements by companies with semi-annual reporting. The second is the opinion voiced by Kevin Warsh, the new Fed Chair, that the Fed should provide less guidance to financial markets on future decisions.

    The two stories may seem unconnected, but they have two common features. First, both actions (removing quarterly reporting requirements and reducing/eliminating Fed guidance), if carried through, will remove "news" that markets have become used to receiving and using to calibrate prices.  Second, the arguments for and against each of these proposals have parallels. The advocates for removing quarterly reports argue that they feed into market myopia and increase short-termism in markets, and the supporters of less Fed guidance believe that this guidance creates gaming among traders and investors, increasing focus on the FOMC actions at the expense of fundamentals. The pushback against both proposals comes from those who believe that withholding quarterly reports and Fed guidance removes information that markets use to set prices, making these prices more volatile and less informative. As is almost always the case with these debates, there is both truth and hyperbole on both sides, and I will try to thread the needle.

Earnings Reports

    Most investors and traders in equity markets, and especially so in the United States, have spent their investing lifetimes in an environment where companies not only release full financial statements every quarter, but do so with fan fare. As we will note in this section, that has not always been the case, even in the US, and has more recent origins, with setbacks, in many foreign markets. 

The History of Earnings Reporting Periodicity

   The Securities Exchange Act of 1934, which created the SEC, set up foundational annual reporting requirements (10-Ks) for publicly traded companies, modified in 1955 to require semi-annual reporting and in 1970, quarterly reports, within 45 days from the end of each quarter. That said, there have been forces that have induced firms to report earnings on a more frequent basis to investors well before these regulatory requirements were put in place. The first were the stock exchanges that imposed their own constraints, with the NYSE requiring most firms to report on a quarterly basis as early as 1939. The second was the recognition by firms that financial transparency (in the form of more frequent and more detailed financial reports) could make them more attractive to investors. As a consequence, it is estimated that in 1931, prior to either the SEC or NYSE mandating disclosure, more than 60% of publicly traded companies were already disclosing information on a quarterly basis.

    The shift to more frequent reporting was slower in the rest of the world and has seen more reversals. Europe, for much of the last century, has a patchwork of rules, with some countries adopting stricter disclosure laws than others. The UK imposed mandatory quarterly earnings reporting in 2007, but allowed a shift back to semi-annual reporting in 2014, and the EU also followed a similar timeline, introducing quarterly reports in 2007 and withdrawing that requirement in 2013. In 2003, Singapore started requiring quarterly reporting for firms with market capitalization exceeding S$20 million, but in 2020, shifted away to requiring it only for a subset of firms with financial and regulatory concerns. Japan started its quarterly reporting requirements in 2003 as well, but it too reversed that requirement in 2024.  In emerging markets, there are large variations across countries. In India, publicly traded companies are required to report their financials on a quarterly basis, and the same is true for many Brazilian and Chinese companies. In Africa, Nigeria requires quarterly reporting but South Africa  has a semi-annual reporting mandate, though many companies voluntarily release quarterly financials; much of the rest of Africa has semi-annual reporting requirements. 

    In sum, the belief at the start of the twenty first century that the rest of the world would follow the US model of mandated quarterly reporting for publicly traded companies has not come to fruition, as many parts of the world have experimented with mandatory quarterly reporting, before abandoning it in favor of semi-annual reporting, for a variety of reasons. That said, it is worth noting that a significant percentage of firms voluntarily report their financial results on a quarterly basis, even when not mandated, albeit with different degrees of depth.

The Content of Earnings Reports

    The debate about how frequently companies should report their financials misses a key detail related to what their financial reports include as content. Focusing on the US, for instance, the magnitude of quarterly earnings reports has increased over time, expanding from bare bones financial statements fin the 1970s to much larger documents that go well beyond financial statements today. In 1980, for instance, a typical quarterly earnings report contained 2000-5000 words, but by the turn of this century, those reports had tripled or quadrupled in size, and the trends continue. The graph below, for instance, looks at the growth in word count for the median quarterly and annual reports in the Russell 3000 companies between 2006 and 2020 (for quarterly) and 1994 to 2020 (for annual):

As you can see the number of words in both quarterly and annual reports has increased over time, and the bulking up of earnings reports can be explained by multiple factors:

1. Accounting rule changes: Accounting rule writers have been busy adding more items to the list of required disclosures for public companies in the last few decades. Some of this increased disclosure (stock-based compensation, for example) reflects a changing business world and is merited, some is in reaction to a corporate scandal and if often knee-jerk and some, in my cynical vie, reflects accounting trying to be relevant to markets again. 
2. Macro events: In years of market crises, economic or political, you will see disclosures increase. In the graphs above, notice the spikes in 2008/2009 and 2020, the first in response to the 2008 banking crisis and the latter to COVID.  Superimposing the effects of globalization, where a company finds itself exposed to problems in every corner of the world, it has added to the disclosure bloat.
3. Legal Protection: One of the culprits responsible for disclosures bulk is the risk exposure section, where companies are required lay out an exhaustive (and exhausting) list of things that can go wrong in their business models. I have never found a risk disclosure useful in a valuation, as it seems to be written by lawyers with the objective of providing legal cover.
4. Guidance: In the 1980s, quarterly earnings reports were focused on reporting on operations during the quarter in question and management was not expected to, and did not provide, guidance about future quarters. That started to change in the 1990s, especially with the passage of the 1995 Safe Harbor Law and Reg FD (which prevented companies from selectively leaking information to analysts), and surged through the second half of the decade, peaking in 2003, when more than 50% of all companies providing earnings guidance. Thankfully, the process has receded, with only a fifth of all firms now providing guidance with earnings reports, but it is undeniable that there is much more forward-looking components to earnings reports than used to be the case.

The bulking up of earnings reports is part of a phenomena that I term "disclosure diarrhea" and argue has undercut the usefulness of these reports, with more disclosure perversely making for less information.

The Earnings Game

    To make sense of the arguments for and against quarterly earnings reporting, you have to get a measure of what happens leading into and out of these reports, in the "earnings game". The process starts with analysts and investors making forecasts of what the earnings report will contain, almost always including estimates of the earnings per share, but often also containing estimates of expected revenues and even operating metrics (like margins) for high profile companies. The analyst forecasts, at least from sell side analysts, become quasi public information and are often aggregated and reported as consensus estimates by financial news services. Zacks, for instance, is one of the services that has been doing this for decades, but that information is now widely accessible on Google and Yahoo! Finance (with the estimates for Apple on July 27, 2026, for the September 2026 earnings report, shown below):

Yahoo! Finance for Apple earnings forecasts

These analyst forecasts, once made, are revisited, partly in response to company-specific news stories and partly to macroeconomic developments, and revised forecasts are provided, with services again tracking these revisions for trends (as you can see below for Apple, from Zack's):

Zack's Apple earnings revisions

As the earnings release date approaches, analysts continue to revise their estimates, and on the date of the announcement, the actual earnings per share is compared to the expected number, with higher (lower) than expected earnings labeled as positive (negative) surprises. The market price response is often consistent, with positive (negative) earnings surprises translating into increases (decreases) in stock price. The graph below, while dated, looks at stock price responses to earnings surprised classified into ten deciles (from most positive to most negative):

There is some evidence that the market responses to earnings reports have become more muted over time, perhaps because of public access to analyst forecasts and revisions. The link between earnings surprises and stock price changes has become the reason why analysts spend as much time as they do, forecasting earnings per share in the next quarterly report, and why traders focusing on the same metric. There is another aspect of the market reaction to earnings surprises that becomes grist for the trading mill, and it comes from the price drifts in the days after earnings are released, with positive (negative) surprises followed by upward (downward) drifts. While the price drift is small, it may still be large enough to make a difference in active trading, where winning by inches is still winning.

    As with almost everything else that is market-related, there are no easy wins in this game, and as more and more people play the earnings forecasting game, new wrinkles have emerged. First, companies have learned to use the flexibility embedded in accounting rules to find ways to beat analyst estimates, with tech companies, in particular, standing out. That earnings gaming plays out as a disproportionately large number of positive earnings surprises (at least among the S&P 500 companies, broken down by sector), as is clear from earnings surprises at the  S&P 500 companies in the second quarter of 2026:

Source: Factset

Second, as companies routinely beat analyst estimates, markets readjust, creating the phenomenon of whispered earnings, where investors build in the expectation that a company that has historically delivered earnings that are 5% or 10% above estimates will continue to do so, and a lesser number is a negative surprise. In the graph below, I look at the market price reaction to earnings surprises in the second quarter of 2026:

Source: Factset

As you can see, the linkage between earnings surprises and price reaction is weak, with a significant subset of positive surprises resulting in price drops. 

The Bottom Line

    Much of the debate about whether the US should shift away from quarterly to semi-annual reports can be boiled down to what you think about the time and energy investors and companies spend playing the earnings game, and where that time and energy will be spent in the absence of quarterly reports. Those who are advocates for less frequent reporting are of the view that the earnings game, focused as it is on next quarter's earnings estimates and whether the company can beat them, contributes to short-termism and distracts from fundamentals. Those who are pushing for preserving the status quo (of quarterly reporting) believe that removing quarterly reports will just shift the game, perhaps more intensively, into the semi-annual reports and that there is value to long term investors from having quarterly reports, gaming notwithstanding. 

    There is another issue that comes up in the context of quarterly reporting, and what would happen if these reports did not exist. Legal strictures notwithstanding, insiders (from within and outside the firm) trade and make money on material information that they have access to, but the public does not. Removing quarterly reporting will create more of an opening for insiders to make money at the expense of public market investors, and while inside trading may contribute to making prices more informative, it also adds to the sense that financial markets are an unfair game.

    I am an investor, and I  think that there is a compromise solution that draws on both sides of this argument. I like quarterly reporting for two reasons. 

1. There is information in those reports that allows me to update my company valuations, though for many companies, the marginal impact of a quarterly report on value is small. 
2. While I have no interest in playing the earnings game, the price corrections that happen around earnings reports serve two purposes. For companies that I have a position in, they can operate as catalysts, bringing down (up) the stock price of over valued (under valued) companies. At the same time, almost all of the information that I find useful in an earnings reports is in the financial statements and footnotes, not in the lengthy discussions of risk exposure or in the management guidance, and I would welcome an elimination of these sections and a slimming down of these reports. 

Note that none of my arguments for preserving quarterly reporting are about short-termism, and that is intentional. First, I am not sure what short-termism even means, since the cynical answer seems to be that any market movement away from your preferred price direction is short term, and any movement in your favor is indicative of market wisdom. Second, I believe that most market participants, and this is true across time and markets, trade to make money in the near term, and that there is nothing that regulators or rule writers can do to alter this dynamic. In fact, the magic of markets is that millions of trades motivated by opportunism and the short term can still yield a price that is long term and rational. Finally, it remains true that if we were all long-term investors who traded only when the fundamentals drove us to do so, markets would be less liquid and transactions costs would increase; short term traders provide a market service and supply liquidity that we all (including long term investors) benefit from.

    I hope that the SEC preserves the current quarterly reporting requirement, while scaling back the volume of disclosure, but if it decides otherwise, it will not materially change much of what I do. I will miss the quarterly updates more with younger, higher-growth firms, where the operating metrics (revenues, margins etc.) can change quickly over short periods, but it is my guess that many of these firms will voluntarily continue the quarterly reporting tradition. 

Fed Guidance on Rates

    For most investors who started investing after 2008, the Fed, in particular, and central banks, in general, have loomed large in the investing process. Many investors attribute the low interest rates after 2008 almost entirely to Fed actions, and by extension, blame the Fed for the higher rates since 2022. I have long argued that not only is this perception incorrect, but that it is unhealthy for investors to view the Fed as either savior or villain. 

A Short (and Personal) History of the Fed

    I started in equity markets in the 1980s, when Paul Volcker as the chair of the Fed played a central role in getting inflation back into check. I might have been ignorant, but I did not know the names of any of the members of the Federal Open Market Committee and had no idea when they met. Changes in the fed funds rate, the only rate effectively controlled by the FOMC, would percolate their way into markets, but I don't remember them being central to equity market movements. 

    Volcker was followed by Alan Greenspan, and while he acquired rockstar status (at least  among investors) in the late 1990s, his views on rates were superseded by his views on equity investors (and their irrational exuberance). The FOMC met eight times per year during that period, and you can access the meeting minutes and actions on the Fed website here, but it stayed away from explicit guidance about future rate changes, choosing to send subtle hints instead. 

    The sea change in Fed behavior and centrality occurred with the 2008 market crisis, when the Fed first introduced explicit guidance noting that rates would stay low "for some time", and it has largely continued that practice through the stewardships of Bernanke (2006-2014), Yellen (2014-2020) and Powell (2020-2026). Along the way, its place in markets has changed, as both bond and equity investors have become focused on the Fed as the arbiter of interest rates and director of the economy. 

The Fed's Powers (and Powerlessness)

    To understand the extent and limits of the Fed's capacity to guide rates and the economy, it is useful to begin with an understanding of what it does. Through its twelve districts that span the United States, the Fed collects information on almost every aspect of the economy, from price pressures building on consumers and producers to the pace of economic growth. While there are other government agencies that also track these statistics, it is undeniable that the Fed has a big picture view and access to more data than any other government agency. The Federal Open Market Committee, composed of all of the members of the board of governors and representatives of the district presidents, sets Fed policy on open market operations (where the Fed buys and sells US government securities), the size of the Fed's balance sheet and the Fed Funds rate (an overnight rate at which banks can borrow and lend their reserves). In addition to the FOMC providing policy direction on inflation and the economy, the Fed chair testifies to Congress every six months, facing and answering questions from legislators.

    As the key interest rate set by the Fed, the Fed Funds rate often acquires an outsized role and there are good reasons to pay attention to it. First, it operates as a signal of what the Fed is seeing in the data it has collected on the economy, with an increase (decrease) in rates indicating that it sees higher (lower) inflation and an overheated (slowing) economy. Second, there are interest rates that are directly tied to the Fed Funds rate, where changes percolate down to businesses and customers; the prime rate and some credit card and CD rates move with the Fed Funds rate. That said, I believe that Fed's capacity to affect interest rates is far more limited than most believe, for two reasons. First, while there is positive correlation between Fed Funds rates and short-term market-set rates (like the US treasury bill rate), there is as much evidence (if not more) that the latter lead the former, rather than the other way around. Put simply, Fed funds rates tend to be increased (cut) after short term treasury rates have gone up (down), suggesting that the Fed is mimicking the market. Second, the relationship between Fed Funds rates and long-term market-set rates, which drive asset valuation and affect borrowers more, is even weaker. To back these contentions, I chart the effective fund funds rate, the three-month US treasury bill rate and the 10-year treasury note rate on a monthly basis from January 1962 to June 2026:

Download data

At the bottom of the graph, I have a table where I look at the data on a quarterly basis, and break it down into three groups - quarters where the fed funds rate decreased, quarters where it increased and quarters where it stayed unchanged. With both fed funds rate increases and decreases, you can see that the link with short term rates is stronger, and with both short term and long term rates, the bulk of the change in rates happens prior to or in the quarter that the Fed Funds rate changed, but there is only a mild spill over into the quarter after, with three month rates, and almost no spillover, with long term rates. Put simply, baed on this history, it looks like changes in fed funds rate are less signals of future movements in interest rates and more reflectors of changes that have already happened.

    The Fed's weaknesses in setting interest rates also plays out in its capacity to alter the trajectory of the real economy. While there are clearly periods that you can point to where Fed actions have had a material impact on he economy, with the Fed Fund rate was hiked to 20% under Paul Volcker in 1981, and triggering a deep recession, being a prime example, the link between Fed Fund rates and economic growth remains tenuous. In the graph below, I look at the changes in Fed Funds rates and real GDP growth in the quarter leading into, the quarter of and the quarter after the change:

Download data (FRED)

Again, there is little backing for conventional wisdom, which is that fed tightening (by raising the Fed funds rate) leads to drops in real growth (or even recessions) and that fed loosening (by lowering the Fed funds rate) is a signal of higher economic growth in the future. In fact, the more general conclusion that one can draw from the data is that the fed effect on the real economy has been more "meh" than "wow".

    The gap between investor perception on what the Fed can control on interest rates and the economy and its actual powers is not just wide, but potentially dangerous. From a policy perspective, it can lead to perverse actions, where central banks are pressured to lower the rates they control (like the Fed Funds rate) in the face of high inflation, leading to even higher inflation in the future. From an investor and business perspective, the focus on what the Fed is doing or will do can take attention away from the fundamentals, especially inflation, that ultimately drive both interest rates and growth.

Download data

As you can see, much of the variation in long term interest rates (with the ten-year US treasury rate standing in as proxy) can be explained by movements in inflation and real economic growth over time, not Fed action or inaction.

The Warsh Doctrine?

    All Fed chairs have had to wrestle with the problem of being perceived as all-powerful, when their true powers are limited, but Kevin Warsh is perhaps more exposed than any of his predecessors. The market fixation with the Fed is now deeply embedded in market, and there are politicians on both sides of the aisle who seem to think that Warsh can bring rates (mortgage, treasury) down to 2% or lower, if he so desires, when the truth is that with inflation expectations running at 2.5-3%, there is no chance of that happening. 

    The pathway out of this problem will be long and there will be pushback, but the end game should be a world where you see and hear from the Fed less, not more. I do believe that the decision to reduce or withhold guidance is a good first step, and it has to be followed by more open humility from the Fed (and from Warsh) about the limits of its powers and honesty about how frequently it follows markets, rather than leads them. In the context of today's (July 29, 2026) decision by the FOMC to leave rates unchanged, for instance, the subtext is that while inflation is running hotter than desired (3% or more, as opposed to the targeted number of 2%), much of that inflation is being driven by a war and its effect on oil prices

    There will be some who feel that markets will be lost without Fed guidance, but I don't think so. After all, financial markets set interest rates and stock prices before the guidance era, and did a pretty good job. In fact, I think that the surge in guidance from the Fed has led many in markets to abdicate their responsibility for paying heed to fundamentals and gauging what interest rates should be. 

Conclusion

    If there is a takeaway from this post, it should be that there is nothing inherently good about having more disclosure. In fact, there is a tipping point, where information overload can cause investors to behave in perverse ways. Thus, I am less of an absolutist about the quarterly versus semi-annual reporting debate than some, though my view is that rather than reduce the frequency of reporting, the SEC should be looking at slimming down reports, by replacing one-size-fits-all disclosure requirements with targeted disclosures and keeping the focus on reporting what has happened rather than prognosticate about the future. 

    With the Federal Reserve too, I think less is more is a better strategy - less guidance from the Fed about what it will do in the future, less opining from FOMC members about interest rates and the economy and less attention to FOMC meetings and the smoke signals that emerge from these meetings. Markets will step in to fill the vacuum, and that is good not just for investors but for the Fed, since its decisions are informed by those market judgments.

YouTube Video

Data

1. Fed Funds Rates and US Treasury rates, by month (1962-2026)
2. Fed Funds Rates and Real GDP growth, by quarter (1962-2026)
3. Intrinsic risk free rates and 10-year Treasuries (1954-2025)

Country Risk: Drivers, Measures and Investment Implications - The 2026 Edition!

    I am a creature of habit in my personal and professional life, and in the context of the content that I post online, there is a ritual that I follow with my data updates. I start the year with my general data update online, and follow up with a series of posts where I examine the implications of this data for investing and corporate finance.  Since 2008, I have also done annual update papers on equity risk premiums in March of each year, with the link to the 2026 update here, and country risk in July of each year, where I look at the topics in more details, trying as best as I can to integrate the data, research and my own thinking. This year's country risk update paper is now available, and as in prior years, I will spend this post looking what causes risk to vary across countries, how to measure those risk variations and the implications for businesses and investors.

Country Risk: Relevance

  In my years as a business school student, country risk was given short shrift and I don't remember spending much time talking or thinking about it. Part of the reason was that business school education  was dollar-centric and built on the presumption that most graduates would go to work in New York, London or Tokyo, and have little need to confront country risk on a day-to-day basis. For those who raised country risk as an issue, the response was that you could, as a company or investor with global exposure, diversify it away. Both presumptions were wrong even then, and have become even more flawed over time as we have sold both companies and investors on the benefits of globalization.

    For businesses, the exposure to country risk comes from both the revenue side, as larger portions of every company's revenues come from foreign markets, and the cost side, as production gets outsourced to locales overseas. That exposure tends to increase as companies scale up, and is higher in some sectors than others; technology companies, for instance, get far more of their revenues from other non-domestic markets than manufacturing or service businesses. Outside of utilities (power, water), it is rare for a company to be entirely domestic-focused on both its revenue and cost sides. For investors, the initial draw of investing in foreign markets might have been diversification but the greater pull has come from greed, i.e., the belief that you can higher returns in the rest of the world. That process was accelerated by the creation of investment vehicles (index and mutual funds) that made investing overseas easier, the lowering of transactions costs across markets and a greater standardization of financial statements and disclosure across the globe. The home bias in portfolios, i.e., the skewing of portfolios towards domestic market investments, has not disappeared but it is lower than it was at the turn of the last century.

   The notion that country risk is diversifiable, i.e., that if you are operating or investing across the world, the risks will average out across countries, has been undercut by the increased correlation across global equity markets, and especially so during market crises (which is when you care the most).  At the risk of being hyperbolic, there is no place to hide from country risk, for either businesses or investors, and ignoring or dismissing country risk is not an option. I discovered this truth in the 1990s, when I found myself in need of a mechanism to incorporate country risk into my corporate financial analysis and valuations, and the process that you see described in this post was born from that need. I would hasten to add that the process that I describe has very little intellectual firepower behind it, puts pragmatism ahead of theory and most importantly is a work-in-process.

Country Risk: Drivers

    I don't think that there would be much disagreement, if I assert that it is riskier to invest in some parts of the world than others, but there is likely to be plenty of disagreement on why there are risk differences and which parts of the world are riskiest. In the broadest sense, I argue that variation in business risk across countries can be traced to four factors - the political structure of the country (democracy vs authoritarian), the prevalence of corruption in the country (operating as a hidden tax and distorting business outcomes), the extent of violence in the country (from internal and external forces) and the strength of the legal system in enforcing property rights and contractual obligations. 

    On the political risk front, I looked at the EIU's Democracy Index, a composite score measuring both political freedom and protections of civil liberties, with the caveat that any index that tries to measure these will make subjective judgements that not everyone will agree with. In their most recent update, here is what the EIU scores looked like around the world:

Source: Economist 
Low (High) score: Least (Most) freedom

Based on these scores, the tilt towards authoritarianism has increased over the last decade, with only 7.3% of the world's population living in democracies at the end of 2025. Note, though, that there is still an open question of whether businesses and economies do better under democratic than authoritarian regimes, and the answer in the research is at best a "maybe".  From a risk perspective, democratic regimes create more continuous risk for businesses, with elections bringing regulatory and rule changes to economies, than authoritarian regimes, where governments can promise more continuity in policy, but when change does come to the latter, it is more likely to be large and wrenching.

    

    Corruption is a fact of life in much of the world, and businesses often have no choice but to pay the price to survive and grow. Transparency International, a global coalition against corruption, tries to capture the extent of corruption, comes up with corruption scores for countries, with lower scores indicating less corruption, and the most recent edition contains the following:

Source: Transparency International
Low (High) score: Most (Least) corruption

Northern Europe has the lowest corruption scores, followed by Canada, United States and Australia, but large portions of Africa have high exposure to corruption, with Latin America and much of Asia falling in the middle. 

    Living in the midst of violence takes a toll, and that toll is extracted from businesses that try to operate in its presence. Vision of Humanity computes peace scores for countries, measuring exposure to both violence within the country as well as from wars and terrorism. The most recent peace scores are reported below:

Source: Vision of Humanity
Low (High) score: Most (Least) peaceful

Canada, Australia, Japan and much of Europe score high on the peace dimension, and while Latin America and Africa score lower, there are portions of each continent that are more peaceful. The Russia-Ukraine war has created a huge area of violence across Eastern Europe and Russia, and exposure to gun violence creates a drag on the United States.

    Businesses are dependent on the legal system  to enforce property rights as well as contractual obligations. Countries that have legal systems that are either capricious on these fronts, or hopelessly slow in acting, create challenges for businesses that operate in them, creating both costs and risks that they otherwise would not face. Property Rights Alliance is an entity that tracks international property rights across the world, and in their most recent update, their property rights scores by country are captured below:

Source: International Property Rights
Low (High) score: Least (Most) property rights

There are wide differences across regions, when it comes to legal and property rights, with Latin America, Africa and Asia lagging and Europe, Australia and much of North America leading. 

    There is one final dimension that I have added to country risk in recent years that captures exposure to climate risk. While there are many different entities that measure this exposure, each one with its own skews, the map below which shows the climate risk exposure, by country, from GermanWatch:

Source: GermanWatch
Low (High) score: Least (Most) affected

There are two reasons why climate risk has not become a bigger topic in country risk discussions. The first is that there is no part of the globe that is unaffected, making it less of a differentiator across countries on the risk dimension. The second is that climate risk, by itself, is an abstraction for businesses, until it starts affecting the bottom line, and while there are individual companies that are being impacted, the aggregate effects, at least at the moment, are not big enough to change the discussion. 

 Country Default Risk

    While country risk is determined by multiple factors, the challenge that businesses is  in consolidating all of those risks into one number. The market that does this most directly is the debt market, where, when countries (sovereigns) seek to borrow money, lenders determine the interest rates to charge them, based upon perceived default risk. To understand why lenders worry about default with sovereign debt, you can start by looking at the history of sovereign defaults in the graph below:

Source: BoC & BoE Sovereign Default Database

Debt defaults, which soared in the 1980s and 1990s, have been lower in this century, with a shift away from loan defaults (where banks are usually the lenders) to defaults in the bond market. It is also worth noting that a non-trivial portion of sovereign defaults in each year are local currency defaults, indicating that for some borrowers, the costs of defaulting are viewed as smaller than the costs of inflation arising from printing more currency to pay off debt. Over time, Latin America has been the epicenter for sovereign default, but at the end of 2023, sovereign debt in default had a wide geographical spread:

Source: BoC & BoE Sovereign Default Database

The most widely accessible measures of sovereign default risk remain sovereign ratings, with ratings agencies operating as (imperfect) arbiters. At the start of July 2026, the graph below reports the sovereign ratings for all rated countries, from S&P, Moody's and Fitch:

Source: Multiple public sources

As you can see, the ratings agencies mostly agree on their assessments of default risk, and sovereign ratings are correlated with the risk drivers (politics, corruption, violence, legal system) that we outlined in the last section. I do believe that ratings agencies, notwithstanding the critiques of bias and mis-measurement leveled against them, do a reasonably good job in their ratings assessments, but they are often slow to act, when confronted with change. 

    The sovereign CDS market offers a market-based alternative for measuring sovereign default risk, with investors making assessments of how much they would demand to insure against sovereign default in the form of (annualized) spreads. In the graph below, I list 10-year sovereign CDS spreads as of July 1, 2026:

Source: Bloomberg

Note that sovereign CDS spreads are available for only 84 countries, and that there are swaths of the world (Central and North Africa, frontier markets) where they are not available. 

Country Composite Risk

    When you lend money to governments or buy government bonds, sovereign default risk is your key concern, and both sovereign ratings and CDS spreads try to measure that risk. When running a business in a country, you are exposed to a much wider range of risks, and measuring exposure to those risks may require different measures. One alternative is country risk scores, where services evaluate how  countries measure up on different risk drivers, and come up with composite scores for these countries. In the table below, I report the country risk scores from two services - Political Risk Services (PRS) and the Economist (EIU), at the start of July 2026:

Sources: EIU (Economist) and PRS

The table illustrates three problems that you face with political risk scores. The first is that the scoring is idiosyncratic, with the Economist going from low scores for the safest countries to high scores for the riskiest, and PRS doing the reverse. The second is that each service picks different factors to consider, and different weightings, leading to scoring divergences that sometimes confound; PRS, for instance, ranks the United States as riskier than Ghana, on a composite risk basis. The third is that the scores, by themselves, are difficult to convert into inputs in financial analysis, either in cash flow or discount rate adjustment.

   It is to combat the third problem that I started estimating country equity risk premiums, and while the details of the process and the data that I use have changed over the last three decades, the basic structure has remained unchanged. I start with an estimate of the equity risk premium for a mature market, and build a country risk premium, if needed, for riskier countries. Until 2025, I estimated the mature market premium by computing an implied equity risk premium for the S&P 500, and using that as the base, arguing that the US, as a Aaa rated country (at least according to Moody's), represented a mature market. The Moody's downgrade for the US, from Aaa to Aa1, has thrown a wrench into that approach, requiring adaptation. In response, I now start with an estimate of the implied ERP for the S&P 500, but then adjust that estimate for the default spread (based on the Aa1 rating) for the US, with the resulting values at the start of July 2026 below:

Spreadsheet: https://pages.stern.nyu.edu/~adamodar/pc/implprem/ERPJuly26.xlsx

As you can see, with the S&P 500 at 7499.36 on July 1, 2026, the implied equity risk premium for the United States is 4.42%, and netting out the default spread of 0.22% for the Aa1 rating yields a mature market premium of 4.20%.

    To estimate country risk premiums, I start with the sovereign ratings for rated countries and convert those ratings into default spreads. To adjust for the higher risk associated with equities, relative to government bonds, I estimate a composite measure of that relative risk, by scaling the volatility in an emerging market equity index to the volatility in a emerging market government bond ETF, and scale the default risk up with this relative risk measure (1.55 in July 2026) to get country risk premiums:

For the two dozen countries that have no sovereign ratings, I adopt an even more makeshift approach, where I used political risk scores for these countries, and then looked for rated countries with similar scores. The table below has equity and country risk premiums, by country, for all of the countries that I evaluated in July 2026:

Download data

I did post an earlier version of this table a couple of weeks ago, but the numbers that I reported reflected in incomplete update of sovereign default spreads, and this table (and the data on my webpage) now reflect the corrected (and lower) spreads. (As a solo act, I am deeply grateful for the checking that those who use my data do, and thankful when they point out mistakes that I have made.)

Company Exposure to Country Risk

    If you buy into my argument that every company has a narrative, and it is the narrative that drives its value, it is worth considering where country risk fits into that narrative. The answer, I believe, comes from looking at where the country in question falls in the life cycle:

The message from this life cycle view is a sobering one, especially for those analyzing companies that operate in very risky countries, since the narratives for these companies implicitly or explicitly incorporate a country risk component. You cannot value a Venezuelan company without taking a strong view about Venezuela, or even an Indian and Brazilian company without an India or Brazil country story underpinning value. In contrast, you may be able to value US and European companies, without explicitly considering the evolution of country risk in those parts of the world.

    When looking at an individual company, I believe that country risk exposure comes less from where the company is incorporated and more from where it operates. It is undeniable that companies around the world have substantial exposure outside their domestic markets, and that exposure has increased over time. In the graph below, I look at the revenue breakdown of companies in four indices - the S&P 500 (US), the FTSE 100 (UK), the Nikkei 225 (Japan) and the Sensex (India):

    

In every single index, companies that comprise that index get a significant portion of their revenues from outside the domestic market. Looking across sectors, exposure to foreign markets varies widely with technology companies often generating more than half of their revenues outside their domestic settings. I believe that equity risk premiums for companies should reflect exposure to foreign markets, though it is worth debating how best to weight that exposure - revenues work well for consumer product and service companies, production works better for natural resource companies and a mix of revenues and production may be the right choice for manufacturing companies:

With this framework, you can see why almost all analysts will confront country risk, sooner or later, no matter where they operate in the world and which companies they analyze. 

    For companies, country risk will also come into play when faced with capital budgeting decisions, where they need estimates of hurdle rates for individual projects, to decide where to invest. For a multinational operating in many businesses, the project cost of equity will have to then also reflect the business the project is in, in addition to country risk. Thus, the cost of equity for a Siemens Appliances for a project in India should reflect the beta for the appliance business, in addition to the country risk for India. In contrast, a Siemens power tool project in Hungary should be computed using the beta for an power tools project and the country risk for Hungary. It is also possible that country risk is not easy to isolate, if the production facilities are in one country but revenues are generated in another. If the Siemens appliance factory in India will be producing products that will be sold in Japan, should we be showing the country risk of India or Japan in the cost of equity calculation? The answer, as was the case in the earlier section on valuation, is that it depends on where the company sees risk coming from. If the risk is that production will be delayed or disrupted by political and economic risk in India, it is Indian country risk that should be looked at, but if the primary concern is that revenues in Japan will be volatile because of economic conditions there, it is Japanese country risk that matters more. If both risks are considerations, you should use a weighted average of Indian and Japanese country risk.

Currency Questions

    For some of you, it may seem odd that I have spent almost an entire post talking about country risk without bringing up currencies. The reason is simple. Currencies are measurement mechanisms, and while they may be affected by the same political and economic factors that drive country risk, they don't determine country risk and in my view, should not command risk premiums, on their own. 

    It is true that hurdle rates are affected by both the equity risk premiums that you estimate and the riskfree rate that you use, and that riskfree rates vary across currencies. In the figure below, I estimate riskfree rates in about 40 currencies, where a local-currency government bond rate is present, and I adjust that government bond rate for the default risk of the government in question:

When estimating the cost of equity for a Turkish project or company in Turkish lira, we start with a riskfree rate in excess of 20% and build on it, by adding equity risk premiums to it, but the cost of equity for the same project or company in Euros will begin with a riskfree rate close to 3% (the German Euro bond rate) and arrive at a much lower number. While this may sound farfetched, the value that you derive for the project or company should be the same using either currency, if you are consistent about estimating your cash flows in the same currency:

Since much or almost all of the differences in riskfree rates come from inflation differentials, matching the high Turkish lira discount rate with a high growth in cashflows in Turkish lira, and the low Euro discount rate with the low growth in cashflows estimated in Euros will yield results that are consistent.

    If you do want to estimate riskfree rates in currencies where there is either no local currency government bond that is traded or where you mistrust the government bond rate, because of light trading or government intervention, the fact that riskfree rate differences across currencies can be tied to differential inflation can be used for estimation; the riskfree rate in any currency can be computed from a base currency (dollar or Euro) riskfree rate and the difference in expected inflation between the local and base currencies:

Put simply, if the expected inflation rate and riskfree rate in US dollars are 2.5% and 4% respectively, and the expected inflation rate in Brazil is 10.5%, the riskfree rate in Brazilian reais should be roughly 12%. The implication of this approach is that currency pegs, when they do exist, will hold only if the inflation in the pegged currency matches or is close to the inflation in the index currency to which it is pegged. It is true that the estimates of riskfree rates will only be as good as the expected inflation rates that are embedded in the estimation, but the good news is that being wrong on expected inflation will be largely offsetting, since both your cashflows and your discount rates will be wrong in the same direction; if you underestimate expected inflation, you will underestimate (overestimate) your riskfree and hurdle rates, but you will also underestimate (overestimate) your expected growth rate in cash flows.

Conclusion

   One of the side effects of the rise of globalization is that there are fewer and fewer companies that are entirely local-country focused in both their revenues and production, and as a result, almost every business and investor is exposed to risk in other parts of the world. The problem with measuring country risk is that while its consequences are economic, it has its sources in history, politics and governance structures. The measures of country risk, whether they be entity-based like sovereign ratings, or market estimates like sovereign CDS spreads, reflect this interplay.

    I confess that I have made simplistic assumptions and cut corners in my attempt to estimate equity risk premiums, by country, and there will be individual countries, perhaps even your own, where you might disagree with my assessments. As I noted earlier, my estimation approach remains a work-in-progress and I am always open to suggestions on how to estimate these premiums better, but keep in mind that whatever those improvements may be, they will have to work across 180 countries. 

YouTube Video

Papers on country risk and equity risk premiums

1. Country Risk Premiums - The 2026 Edition (July 2026)
2. Equity Risk Premiums - The 2026 Edition (March 2026)

Data

1. Equity Risk Premiums, by country - July 2026
2. Implied Equity Risk Premiums for the S&P 500 (Annual since 1960 & Monthly since Sept 08)
3. Inflation-based riskfree rates, by currency - July 2026

Spreadsheet

1. Implied ERP for S&P 500 (July 2026)