The Power of Expectations: Nvidia's Earnings and the Market Reaction!

    Last Wednesday (August 28), the market waited with bated breath for Nvidia’s earning call, scheduled for after the market closed. That call, at first sight, contained exceptionally good news, with revenues and earnings coming in at stratospheric levels, and above expectations, but the stock fell in the aftermath, down 8% in Thursday’s trading. That drop of more than $200 billion in market capitalization in response to what looked like good news, at least on the surface, puzzled market observers, though, as is their wont, they had found a reason by day end. This dance between companies and investors, playing out in expected and actual earnings, is a feature of every earnings season, especially so in the United States, and it has always fascinated me. In this post, I will use the Nvidia earnings release to examine what news, if any, is contained in earnings reports, and how traders and investors use that news to reframe their thinking about stocks.

Earnings Reports: The Components

   When I was first exposed to financial markets in a classroom, I was taught about information being delivered to markets, where that information is processed and converted into prices. I was fascinated by the process, an interplay of accounting, finance and psychology, and it was the subject of my doctoral thesis, on how distortions in information delivery (delays, lies, mistakes) affects stock returns. In the real world, that fascination has led me to pay attention to earnings reports, which while overplayed, remain the primary mechanism for companies to convey information about their performance and prospects to markets.

The Timing

    Publicly traded companies have had disclosure requirements for much of their existence, but those requirements have become formalized and more extensive over time, partly in response to investor demands for more information and partly to even the playing field between institutional and individual investors. In the aftermath of the great depression, the Securities Exchange Commission was created as part of the Securities Exchange Act, in 1934, and that act also required any company issuing securities under that act, i.e., all publicly traded firms, make annual filings (10Ks) and quarterly filings (10Qs), that would be accessible to investors.

    The act also specifies that these filings be made in a timely manner, with a 1946 stipulation the annual filings being made within 90 days of the fiscal year-end, and the quarterly reports within 45 calendar days of the quarter-end. With technology speeding up the filing process, a 2002 rule changed those requirements to 60 days, for annual reports, and 40 days for quarterly reports, for companies with market capitalizations exceeding $700 million. While there are some companies that test out these limits, most companies file well within these deadlines, often within a couple of weeks of the year or quarter ending, and many of them file their reports on about the same date every year.

    If you couple the timing regularity in company filings with the fact that almost 65% of listed companies have fiscal years that coincide with calendar years, it should come as no surprise that earnings reports tend to get bunched up at certain times of the year (mid-January, mid-April, mid-July and mid-October), creating “earnings seasons”. That said, there are quite a few companies, many of them high-profile, that preserve quirky fiscal years, and since Nvidia’s earnings report triggered this post, it is worth noting that Nvidia has a fiscal year that ends on January 31 of each year, with quarters ending on April 30, July 31 and October 31. In fact, the Nvidia earnings report on August 28 covered the second quarter of this fiscal year (which is Nvidia's 2025 fiscal year).

The Expectations Game

    While corporate earnings reports are delivered once a quarter, the work of anticipating what you expect these reports to contain, especially in terms of earnings per share, starts almost immediately after the previous earnings report is delivered. In fact, a significant portion of sell side equity research is dedicated to this activity, with revisions made to the expected earnings, as you get closer and closer to the next earnings report. In making their earnings judgments and revisions, analysts draw on many sources, including:

1. The company’s history/news: With the standard caveat that the past does not guarantee future results, analysts consider a company’s historical trend lines in forecasting revenues and earnings. This can be augmented with other information that is released by the company during the course of the quarter.
2. Peer group reporting: To the extent that the company’s peer group is affected by common factors, it is natural to consider the positive or negative the operating results from other companies in the group, that may have reported earnings ahead of your company. 
3. Other analysts’ estimates: Much as analysts claim to be independent thinkers, it is human nature to be affected by what others in the group are doing. Thus, an upward revision in earnings by one analyst, especially an influential one, can lead to revisions upwards on the part of other analysts.
4. Macro news: While macroeconomic news (about the economy, inflation or currency exchange rates) cuts across the market, in terms of impact, some companies are more exposed to macroeconomic factors than others, and analysts will have to revisit earnings estimates in light of new information.

The earnings expectations for individual companies, from sell side equity research analysts are publicly accessible, giving us a window on trend lines. 

    Nvidia is one of the most widely followed companies in the world, and most of the seventy plus analysts who publicly follow the firm play the estimation game, leading into the earnings reports. Ahead of the most recent second quarter earnings report, the analyst consensus was that the company would report revenues of $28.42 billion for the quarter, and fully diluted earnings per share of 64 cents; in the 30 days leading into the report, the earnings estimates had drifted up mildly (about 0.1%), with the delay in the Blackwell (NVidia’s new AI chip) talked about but not expected to affect revenue growth near term. It is worth noting that not all analysts tracking the stock forecast every metric, and that there was disagreement among them, which is also captured in the range on the estimates; on earnings per share, for instance, the estimates ranged from 60 to 68 cents, and on revenues, from $26 to $30 billion.

    The pre-game show is not restricted to analysts and investors, and markets partake in the expectations game in two ways. 

• Stock prices adjust up or down, as earnings expectations are revised upwards or downwards, in the weeks leading up to the earnings report. Nvidia, which traded at $104 on May 23rd, right after the company reported its results for the first quarter of 2024, had its ups and down during the quarter, hitting an all-time high of $135.58 on June 18, 2024, and a low of $92.06, on August 5, before ending at $125.61 on August 28, just ahead of the earnings report:
  
  
  
  During that period, the company also split its shares, ten to one, on June 10, a week ahead of reaching its highs.
  
  
• Stock volatility can also changes, depending upon disagreements among analysts about expected earnings, and the expected market reaction to earnings surprises. That effect is visible not only in observed stock price volatility, but also in the options market, as implied volatility. For Nvidia, there was clearly much more disagreement among investors about the contents of the second quarter earnings report, with implied volatility spiking in the weeks ahead of the report: 

Source: Fintel

While volatility tends to increase just ahead of earnings reports, the surge in volatility ahead of the second quarter earnings for Nvidia was unusually large, a reflection of the disagreement among investors about how the earnings report would play out in the market. Put simply, even before Nvidia reported earnings on August 28, markets were indicating more unease about both the contents of the report and the market reaction to the report, than they were with prior earnings releases. 

The Event

    Given the lead-in to earnings reports, what exactly do they contain as news? The SEC strictures that companies disclose both annual and quarterly results have been buffered by accounting requirements on what those disclosures should contain. In the United States, at least, quarterly reports contain almost all of the relevant information that is included in annual reports, and both have suffered from the disclosure bloat that I called attention to in my post on disclosure diarrhea. Nvidia’s second quarter earnings report, weighing in at 80 pages, was shorter than its annual report, which ran 96 pages, and both are less bloated than the filings of other large market-cap companies.

    The centerpieces of the earnings report, not surprisingly, are the financial statements, as operating numbers are compared to expectations, and Nvidia’s second quarter numbers, at least at first sight, are dazzling:

The company’s astonishing run of the last few years continues, as its revenues, powered by AI chip sales, more than doubled over the same quarter last year, and profit margins came in at stratospheric levels.  The problem, though, is that the company's performance over the last three quarters, in particular, have created expectations that no company can meet. While it is just one quarter, there are clear signs of more slowing to come, as scaling will continue to push revenue growth down, the unit economics will be pressured as chip manufacturers (TSMC) push for a larger slice and operating margins will decrease, as competition increases.

    Over the last two decades, companies have supplemented the financial reports with guidance on key metrics, particularly revenues, margins and earnings, in future quarters. That guidance has two objectives, with the first directed at investors, with the intent of providing information, and the second at analysts, to frame expectations for the next quarter. As a company that has played the expectations game well, it should come as no surprise that Nvidia provided guidance for future quarters in its second quarter report, and here too, there were reminders that comparisons would get more challenging in future quarters, as they  predicted that revenue growth rates would come back to earth, and that margins would, at best, level off or perhaps even decline. 

    Finally, in an overlooked news story, Nvidia announced that it would had authorized $50 billion in buybacks, over an unspecified time frame. While that cash return is not surprising for a company that has became a profit machine, it is at odds with the story that some investors were pricing into the stock of a company with almost unlimited growth opportunities in an immense new market (AI). Just as Meta and Alphabet’s dividend initiations signaled that they were approaching middle age, Nvidia’s buyback announcement may be signaling that the company is entering a new phase in the life cycle, intentionally or by accident.

The Scoring

    The final piece of the earning release story, and the one that gets the most news attention, is the market reaction to the earnings reports. There is evidence in market history that earnings reports affect stock prices, with the direction of the effect depending on how actual earnings measure up to expectations. While there have been dozens of academic papers that focus on market reactions to earnings reports, their findings can be captured in a composite graph that classifies earnings reports into deciles, based upon the earnings surprise, defined as the difference between actual and predicted earnings:

As you can see, positive surprises cause stock prices to increase, whereas negative surprises lead to price drops, on the announcement date, but there is drift both before and after surprises in the same direction. The former (prices drifting up before positive and down before negative surprises) is consistent with the notion that information about earnings surprises leaks to markets in the days before the report, but the latter (prices continuing to drift up after positive or down after negative surprises) indicates a slow-learning market that can perhaps be exploited to earn excess returns. Breaking down the findings on earnings reports, there seems to be evidence that the that the earnings surprise effect has moderated over time, perhaps because there are more pathways for information to get to markets.

    Nvidia is not only one of the most widely followed and talked about stocks in the market, but one that has learned to play the expectations game well, insofar as it seems to find a way to beat them consistently, as can be seen in the following table, which looks at their earnings surprises over the last 5 years:

Nvidia Earnings Surprise (%)

Barring two quarters in 2022, Nvidia has managed to beat expectations on earnings per share every quarter for the last five years. There are two interpretations of these results, and there is truth in both of them. The first is that Nvidia, as with many other technology companies, has enough discretion in both its expenditures (especially in R&D) and in its revenue recognition, that it can use it to beat what analysts expect. The second is that the speed with which the demand for AI chips has grown has surprised everyone in the space (company, analysts, investors) and that the results reflect the undershooting on forecasts. 

    Focusing specifically on the 2025 second quarter, Nvidia beat analyst expectations, delivering earnings per share of 68 cents (above the 64 cents forecast) and revenues of $30 billion (again higher than the $28.4 billion forecast), but the percentage by which it beat expectations was smaller than in the most recent quarters. That may sound like nitpicking, but the expectations game is an insidious one, where investors move the goal posts constantly, and more so, if you have been successful in the past. On August 28, after the earnings report, Nvidia saw share prices drop by 8% and not only did that loss persist through the next trading day, the stock has continued to lose ground, and was trading at $106 at the start of trading on September 6, 2028.

Earnings Reports: Reading the Tea Leaves

    So what do you learn from earnings reports that may cause you to reassess what a stock is worth? The answer will depend upon whether you consider yourself more of a trader or primarily an investor. If that distinction is lost on you, I will start this section by drawing the contrast between the two approaches, and what each approach is looking for in an earnings report.

Value versus Price

    At the risk of revisiting a theme that I have used many times before, there are key differences in philosophy and approach between valuing an asset and pricing it.

• The value of an asset is determined by its fundamentals – cash flows, growth and risk, and we attempt to estimate that value by bringing in these fundamentals into a construct like discounted cash flow valuation or a DCF. Looking past the modeling and the numbers, though, the value of a business ultimately comes from the story you tell about that business, and how that story plays out in the valuation inputs.
• The price of an asset is set by demand and supply, and while fundamentals play a role, five decades of behavioral finance has also taught us that momentum and mood have a much greater effect in pricing, and that the most effective approach to pricing an asset is to find out what others are paying for similar assets. Thus, determining how much to pay for a stock by using a PE ratio derived from looking its peer group is pricing the stock, not valuing it.

The difference between investing and trading stems from this distinction between value and price. Investing is about valuing an asset, buying it at a price less than value and hoping that the gap will close, whereas trading is almost entirely a pricing game, buying at a low price and selling at a higher one, taking advantage of momentum or mood shifts. Given the very different perspectives the two groups bring to markets, it should come as no surprise that what traders look for in an earnings report is very different from what investors see in that same earnings report. 

Earnings Reports: The Trading Read

    If prices are driven by mood and momentum, it should come as no surprise that what traders are looking for in an earnings report are clues about how whether the prevailing mood and momentum will prevail or shift. It follows that traders tend to focus on the earnings per share surprises, since its centrality to the report makes it more likely to be a momentum-driver. In addition, traders are also swayed more by the theater around how earnings news gets delivered, as evidenced, for instance, by the negative reaction to a recent earnings report from Tesla, where Elon Musk sounded downbeat, during the earnings call. Finally, there is a significant feedback loop, in pricing, where the initial reaction to an earnings report, either online or in the after market, can affect subsequent reaction. As a trader, you may learn more about how an earnings report will play out by watching social media and market reaction to it than by poring over the financial statements.

    For Nvidia, the second quarter report contained good news, if good is defined as beating expectations, but the earnings beat was lower than in prior quarters. Coupled with sober guidance and a concern the stock had gone up too much and too fast, as its market cap had increased from less than half a trillion to three trillion over the course of two years, the stage was set for a mood and momentum shift, and the trading since the earnings release indicates that it has happened. Note, though, that this does not mean that something else could not cause the momentum to shift back, but before you, as an Nvidia manager or shareholder, are tempted to complain about the vagaries of momentum, recognize that for much of the last two years, no stock has benefited more from momentum than Nvidia.

The Investing Read

    For investors, the takeaways from earnings reports should be very different. If value comes from key value inputs (revenues growth, profitability, reinvestment and risk), and these value inputs themselves come from your company narrative, as an investor, you are looking at the earnings reports to see if there is information in them that would change your core narrative for the company. Thus, an earnings report can have a significant effect on value, if it significantly changes the growth, profitability or risk parts of your company’s story, even though the company’s bottom line (earnings per share) might have come in at expectations. Here are a few examples:

• A company reporting revenue growth, small or even negligible for the moment, but coming from a geography or product that has large market potential, can see its value jump as a consequence. In 2012, I reassessed the value of Facebook upwards, a few months after it had gone public and seen its stock price collapse, because its first earnings report, while disappointing in terms of the bottom line, contained indications that the company was starting to succeed in getting its platform working on smart phones, a historical weak spot for the firm.
• You can also have a company reporting higher than expected revenue growth accompanied by lower than anticipated profit margins, suggesting a changing business model, and thus a changed story and valuation. Earlier this year, I valued Tesla, and argued that their lower margins, while bad news standing alone, was good news if your story for Tesla was that it would emerge as a mass market automobile company, capable of selling more cars than Volkswagen and Toyota. Since the only pathway to that story is with lower-priced cars, the Tesla strategy of cutting prices was in line with that story, albeit at the expense of profit margins.
• A company reporting regulatory or legal actions directed against it, that make its business model more costly or more risky to operate, even though its current numbers (revenues, earnings etc.) are unscathed (so far).

In short, if you are an investor, the most interesting components of the report are not in the proverbial bottom line, i.e., whether earnings per share came in below or above expectations, but in the details. Finally, as investors, you may be interested in how earnings reports change market mood, usually a trading focus, because that mood change can operate as a catalyst that causes the price-value gap to close, enriching you in the process. 

    The figure below summarizes this section, by first contrasting the value and pricing processes, and then looking at how earnings releases can have different meanings to different market participants. 

As in other aspects of the market, it should therefore come as no surprise that the same earnings report can have different consequences for different market participants, and it is also possible that what is good news for one group (traders) may be bad news for another group (investors). 

Nvidia: Earnings and Value

    My trading skills are limited, and that I am incapable of playing the momentum game with any success. Consequently, I am not qualified to weigh in on the debate on whether the momentum shift on Nvidia is temporary or long term, but I will use the Nvidia second quarter earnings report as an opportunity to revisit my Nvidia story and to deliver a September 2024 valuation for the company. My intrinsic valuation models are parsimonious, built around revenue growth, profit margins and reinvestment, and I used the second quarter earnings report to review my story (and inputs) on each one:

Nvidia: Valuation Inputs (Sept 2024)

With these input changes in place, I revalued Nvidia at the start of September 2024, breaking its revenues, earnings and cash flows down into three businesses: an AI chip business that remains its central growth opportunity, and one in which it has a significant lead on the competition, an auto chip business where it is a small player in a small game, but one where there is potential coming from demand for more powerful chips in cars, and the rest, including its existing business in crypto and gaming, where growth and margins are solid, but unlikely to move dramatically. While traders may be disappointed with Nvidia’s earnings release, and wish it could keep its current pace going, I think it is both unrealistic and dangerous to expect it to do so. In fact, one reason that my story for Nvidia has become more expansive, relative to my assessment in June 2023, is that the speed with which AI architecture is being put in place is allowing the total market to grow at a rate far faster than I had forecast last year. In short, relative to where I was about a year ago, the last four earnings reports from the company indicate that the company can scale up more than I thought it could, has higher and more sustainable margins than I predicted and is perhaps less exposed to the cycles that the chip business has historically been victimized by. With those changes in place, my value per share for Nvidia in  is about $87, still about 22% below the stock price of $106 that the stock was trading at on September 5, 2024, a significant difference but one that is far smaller than the divergence that I noted last year.

Download spreadsheet

As always, the normal caveats apply. The first is that I value companies for myself, and while my valuations drive my decisions to buy or sell stocks, they should not determine your choices. That is why my Nvidia valuation spreadsheet is available not just for download, but for modification, to allow you to tell your own story for Nvidia, yielding a different value and decision. The second is that this is a tool for investors, not traders, and if you are playing the trading game, you will have to reframe the analysis and think in terms of mood and momentum. Looking back, I am at peace with the decision made in the summer of 2023 to shed half my Nvidia shares, and hold on to half. While I left money on the table, with the half that I sold, I have been richly compensated for holding on to the other half. I am going to count that as a win and move on!

YouTube Video

Links

1. Nvidia 10 Q (for 2nd Quarter 2025)
2. Nvidia Valuation (updated on September 5, 2024)

Beat your Bot: Building your moat against AI

     It seems like a lifetime has passed since artificial intelligence (AI) became the market's biggest mover, but Open AI introduced the world to ChatGPT on November 30, 2022. While ChatGPT itself represented a low-tech variation of AI, it opened the door to AI not only as a business driver, but one that had the potential to change the way we work and live. In a post on June 30, 2023, I looked at the AI effect on businesses, arguing that it had the potential to ferment revolutionary change, but that it would also create a few big winners, a whole host of wannabes, and many losers, as its disruption worked its way through the economy. In this post, I would like to explore that disruption effect, but this time at a personal level, as we are warned that we risk being displaced by our AI counterparts. I want to focus on that question, trying to find the middle ground between irrational terror, where AI consigns us all to redundancy, and foolish denial, where we dismiss it as a fad.

The Damodaran Bot

    I was in the eleventh week of teaching my 2024 spring semester classes at Stern, when Vasant Dhar, who teaches a range of classes from machine learning to data science at NYU's Stern School (where I teach as well), and has forgotten more about AI than I will ever know, called me. He mentioned that he had developed a Damodaran Bot, and explained that it was an AI creation, which had read every blog post that I had ever written, watched every webcast that I had ever posted and reviewed every valuation that I had made public. Since almost everything that I have ever written or done is in the public domain, in my blog, YouTube videos and webpage, that effectively meant that my bot was better informed than I was about my own work, since its memory is perfect and mine is definitely not. He also went on to tell me that the Bot was ready for a trial run, ready to to value companies, and see how those valuations measured up against valuations done by the best students in my class.

    The results of the contest are still being tabulated, and I am not sure what results I would like to see, since either of the end outcomes would reflect poorly on me. If the Bot's valuations work really well, i.e., it values companies as well, or better, than the students in my class, that is about as strong a signal that I am facing obsolescence, that I can get. If the Bot's valuations work really badly, that would be a reflection that I have failed as a teacher, since the entire rationale for my postings and public valuations is to teach people how to do valuation.

Gauging the threat

    In the months since I was made aware of the Damodaran Bot, I have thought in general terms about what AI will be able to do as well or better than we can, and the areas where it might have trouble. Ultimately, AI is the coming together of two forces that have become more powerful over the last few decades. The first is increasing (and cheaper) computing power, often coming into smaller and smaller packages; our phones are now computationally more powerful than the very first personal computers. The second is the cumulation of data, both quantitative and qualitative, especially with social media accelerating personal data sharing. As an AI novice, it is entirely possible that I am not gauging the threat correctly, but there are three dimensions on which I see the AI playing out (well or badly).

1. Mechanical/Formulaic vs Intuitive/Adaptable: Well before ChatGPT broke into the public consciousness,  IBM's Deep Blue was making a splash playing chess, and beating some of the world's greatest chess players. Deep Blue's strength at chess came from the fact that it had access to every chess game ever played (data) and the computing power to evaluate 200 million chess positions per second, putting even the most brilliant human chess player at a disadvantage. In contrast, AI has struggled more with automated driving, not because driving is mechanically complicated, but because there are human drivers on the surface roads, behaving in unpredictable ways. While AI is making progress on making intuitive leaps, and being adaptable, it will always struggle more on those tasks than on the purely mechanical ones.
2. Rules-based vs Principle-based: Expanding the mechanical/intuitive divide, AI will be better positioned to work smoothly in rules-based disciplines, and will be at a disadvantage in principle-based disciplines. Using valuation to illustrate my point,  accounting and legal valuations are mostly rule-based, with the rules sometimes coming from theory and practice, and sometimes from rule writers drawing arbitrary lines in the sand. AI can not only replicate those valuations, but can do so at no cost and with a much closer adherence to the rules. In contrast, financial valuations done right, are built around principles, requiring judgment calls and analytical choices on the part of appraisers, on how these principles get applied, and should be more difficult to replace with AI.
3. Biased vs Open minded: There is a third dimension on which we can look at how easy or difficult it will be for AI to replace humans and that is in the human capacity to bring bias into decisions and analyses, while claiming to be objective and unbiased. Using appraisal valuation to illustrate, it is worth remembering that clients often come to appraisers, especially in legal or accounting settings, with specific views about what they would like to see in their valuations, and want affirmation of those views from their appraisers, rather than the objective truth. A business person valuing his or her business, ahead of a divorce, where half the estimated value of that business has to be paid out to a soon-to-be ex-spouse, wants a low value estimate, not a high one, and much as the appraiser of the business will claim objectivity, that bias will find its way into the numbers and value. It is true that you can build AI systems to replicate this bias, but it will be much more difficult to convince those systems that the appraisals that emerge are unbiased.

Bringing this down to the personal, the threat to your job or profession, from AI, will be greater if your job is mostly mechanical, rule-based and objective, and less if it is intuitive, principle-based and open to biases. 

Responding to AI

   While AI, at least in its current form, may be unable to replace you at your job, the truth is that AI will get better and more powerful over time, and it will learn more from watching what you do. So, what can we do to make it more difficult to be outsourced by machines or replaced by AI? It is a question that I have thought about for three decades, as machines have become more powerful, and data more ubiquitous, and while I don't have all of the answers, I have four thoughts.

1. Generalist vs Specialist: In the last century, we have seen a push towards specialization in almost every discipline. In medicine, the general practitioner has become the oddity, as specialists abound to treat individual organs and diseases, and in finance, there are specialists in sub-areas that are so esoteric that no one outside those areas can even comprehend the intricacies of what they do. In the process, there are fewer and fewer people who are comfortable operating outside their domains, and humanity has lost something of value. It is the point I made in 2016, after a visit to Florence, where like hundreds of thousands of tourists before me, I marveled at the beauty of the Duomo, one of the largest free-standing domes in the world, at the time of its construction. 
   
   
   
   The Duomo built by Filippo Brunelleschi, an artist who taught himself enough engineering and construction to be able to build the dome, and he was carrying on a tradition of others during that period whose interests and knowledge spanned multiple disciplines. In a post right after the visit, I argued that the world needed more Renaissance men (and women), individuals who can operate across multiple disciplines, and with AI looming as a threat, I feel even more strongly about this need. A Leonardo Da Vinci Bot may be able to match the master in one of his many dimensions (painter, sculptor, scientist), but can it span all of them? I don't think so!
2. Practice bounded story telling: Starting about a decade ago, I drew attention to a contradiction at the heart of valuation practice, where as access to data and more powerful models has increased, in the last few decades, the quality of valuations has actually become worse. I argued that one reason for that depletion in quality is that valuations have become much too mechanical, exercises in financial modeling, rather than assessments of business quality and value. I went on to make the case that good valuations are bridges between stories and numbers, and wrote a book on the topic.
   
   
   
   At the time of the book's publication, I wrote a post on why I think stories make valuations richer and better, and with the AI threat looming, connecting stories to numbers comes with a bonus. If your valuation is all about extrapolating historical data on a spreadsheet, AI can do it quicker, and with far fewer errors than you can. If, however, your valuation is built around a business story, where you have considered the soft data (management quality, the barriers to entry), AI will have a tougher time replicating what you do. 
3. Reasoning muscle: I have never been good at reading physical maps, and I must confess that I have completely lost even my rudimentary map reading skills, having become dependent on GPS to get to where I need to go. While this inability to read maps may not make or break me, there are other skills that we have has human beings, where letting machines step in and help us, because of convenience and speed, will have much worse long term consequences. In an interview I did on teaching a few years, I called attention to the "Google Search" curse, where when faced with a question, we often are quick to look up the answer online, rather than try to work out the answer. While that is benign, if you are looking up answers to trivia, it can be malignant, when used to answer questions that we should be reasoning out answers to, on our own. That reasoning may take longer, and sometimes even lead you to the wrong answers, but it is a learned skill, and one that I am afraid that we risk losing, if we let it languish. You may think that I am overreacting, but evolution has removed skill sets that we used to use as human beings, when we stopped using or needing them, and reasoning may be next on the list.
4. Wandering mind: An empty mind may the devil's workshop, at least according to puritans, but it is also the birthplace for creativity. I have always marveled at the capacity that we have as human beings to connect unrelated thoughts and occurrences, to come up with marvelous insights. Like Archimedes in his bath and Newton under the apple tree, we too can make discoveries, albeit much weighty ones, from our own ruminations. Again, making this personal, two of my favorite posts had their roots in unrelated activities. The first one, Snowmen and Shovels, emerged while I was shoveling snow after a blizzard about a decade ago, and as I and my adult neighbors struggled dourly with the heavy snow, our kids were out building snowmen, and laughing.  I thought of a market analogy, where the same shock (snowstorm) evokes both misery (from some investors) and joy (on the part of others), and used it to contest value with growth investing. The second post, written more recently, came together while I walked my dog, and pondered how earthquakes in Iceland, a data leak at a genetics company and climate change affected value, and that became a more general discourse on how human beings respond (not well) to the possibility of catastrophes.  

It is disconcerting that on every one of these four fronts, progress has made it more difficult rather than less so, to practice. In fact, if you were a conspiracy theorist, you could spin a story of technology companies conspiring to deliver us products, often free and convenient to use, that make us more specialized, more one dimensional and less reason-based, that consume our free time. This may be delusional on my part, but if want to keep the Damodaran Bot at bay, and I take these lessons to heart, I should continue to be a dabbler in all that interests me, work on my weak side (which is story telling), try reasoning my way to answers before looking them up online and take my dog for more walks (without my phone accompanying me). 

Beat your bot!

    I am in an unusual position, insofar as my life’s work is in the public domain, and I have a bot with my name on it not only tracking all of that work, but also shadowing me on any new work that I do. In short, my AI threat is here, and I don’t have the choice of denying its existence or downplaying what it can do. Your work may not be public, and you may not have a bot with your name on it, but it behooves you to act like there is one that tracks you at your job.  As you consider how best to respond, there are three strategies you can try:

1. Be secretive about what you do: My bot has learned how I think and what I do because everything I do is public - on my blog, on YouTube and in my recorded classes. I know that some of you may argue that I have facilitated my own disruption, and that being more secretive with my work would have kept my bot at bay. As a teacher, I neither want that secrecy, nor do I think it is feasible, but your work may lend itself better to this strategy. There are two reasons to be wary, though. The first is that if others do what you do, an AI entity can still imitate you, making it unlikely that you will escape unscathed. The second is that your actions may give away your methods and work process, and AI can thus reverse engineer what you do, and replicate it. Active investing, where portfolio managers claim to use secret sauces to find good investments, can be replicated at relatively low cost, if we can observe what these managers buy and sell. There is a good reason why ETFs have taken away market share from fund managers.
2. Get system protection: I have bought and sold houses multiple times in my lifetime, and it is not only a process that is filled with intermediaries (lawyers, realtors, title deed checkers), all of whom get a slice from the deal, but one where you wonder what they all do in return for their fees. The answer often is not rooted in logic, but in the process, where the system (legal, real estate) requires these intermediaries to be there for the house ownership to transfer. This system protection for incumbents is not just restricted to real estate, and cuts across almost every aspect of our lives, and it creates barriers to disruption. Thus, even if AI can replicate what appraisers do, at close to no cost, I will wager that courts and accounting rule writers will be persuaded by the appraisal ecosystem that the only acceptable appraisals can come from human appraisers. 
3. Build your moat: In business, companies with large, sustainable competitive advantages are viewed as having moats that are difficult to competitors to breach, and are thus more valuable. That same idea applies at the personal level, especially as you look at the possibility of AI replacing you. It is your job, and mine, to think of the moats that we can erect (or already have) that will make it more difficult for our bots to replace us. As to what those moats might be, I cannot answer for you, but the last section lays out my thinking on what I need to do to stay a step ahead.

Needless to say, I am a work in progress, even at this stage of my life, and rather than complain or worry about my bot replacing me, I will work on staying ahead. It is entirely possible that I am embarking on an impossible mission, but I will keep you posted on my progress (or absence of it). Of course, my bot can get so much better at what I do than I am, in which case, this blog may very well be written and maintained by it, and you will never know!

YouTube Video

Blog Posts (referenced)

1. Investing and Valuation: Lessons from the Renaissance
2. Stories and Numbers: How a number cruncher learned story telling!
3. The Google Search Curse (my interview)
4. Snowmen and Shovels: Lessons on Investing
5. Catastrophic Risk: Investing and Business Implications

The Corporate Life Cycle: Managing, Valuation and Investing Implications!

As I reveal my ignorance about TikTok trends, social media celebrities and Gen Z slang, my children are quick to point out my age, and I accept that reality, for the most part. I understand that I am too old to exercise without stretching first or eat a heaping plate of cheese fries and not suffer heartburn, but that does not stop me from trying occasionally. For the last decade or so, I have argued that businesses, like human beings, age, and struggle with aging, and that much of the dysfunction we observe in their decision making stems from refusing to act their age. In fact, the business life cycle has become an integral part of the corporate finance, valuation and investing classes that I teach, and in many of the posts that I have written on this blog. In 2022, I decided that I had hit critical mass, in terms of corporate life cycle content, and that the material could be organized as a book. While the writing for the book was largely done by November 2022, publishing does have a long lead time, and the book, published by Penguin Random House, will be available on August 20, 2024, at a book shop near you. If you are concerned that you are going to be hit with a sales pitch for that book, far from it!  Rather than try to part you from your money, I thought I would give a compressed version of the book in this post, and for most of you, that will suffice.

Setting the Stage

    The notion of a business life cycle is neither new nor original, since versions of it have floated around in management circles for decades, but its applications in finance have been spotty, with some attempts to tie where a company is in the life cycle to its corporate governance and others to accounting ratios. In fact, and this should come as no surprise to anyone who is familiar with his work, the most incisive piece tying excess returns (return on invested capital minus cost of capital) to the corporate life cycle was penned by Michael Mauboussin (with Dan Callahan) just a few months ago.

    My version of the corporate life cycle is built around six stages with the first stage being an idea business (a start-up) and the last one representing decline and demise. 

As you can see, the key tasks shift as business age, from building business models in the high growth phase to scaling up the business in high growth to defending against competition in the mature phase to managing decline int he last phase. Not surprisingly, the operating metrics change as companies age, with high revenue growth accompanied by big losses (from work-in-progress business models) and large reinvestment needs (to delivery future growth) in early-stage companies to large profits and free cash flows in the mature phase to stresses on growth and margins in decline. Consequently, in terms of cash flows, young companies burn through cash, with the burn increasing with potential, cash buildup is common as companies mature followed by cash return, as the realization kicks in that a company’s high growth days are in the past.

    As companies move through the life cycle, they will hit transition points in operations and in capital raising that have to be navigated, with high failure rates at each transition. Thus, most idea businesses never make it to the product phase, many product companies are unable to scale up, and quite a few scaled up firms are unable to defend their businesses from competitors. In short, the corporate life cycle has far higher mortality rates as businesses age than the human life cycle, making it imperative, if you are a business person, that you find the uncommon pathways to survive and grow.

Measures and Determinants

    If you buy into the notion of a corporate life cycle, it stands to reason that you would like a way to determine where a company stands in the life cycle. There are three choices, each with pluses and minuses. 

• The first is to focus on corporate age, where you estimate how old a company is, relative its founding date; it is easy to obtain, but companies age at different rates (as well will argue in the following section), making it a blunt weapon.
• The second is to look at the industry group or sector that a company is in, and then follow up by classifying that industry group or sector into high or low growth; for the last four decades, in US equity markets, tech has been viewed as growth and utilities as mature. Here again, the problem is that high growth industry groups begin to mature, just as companies do, and this has been true for some segments of the tech sector.
• The third is to focus on the operating metrics of the firm, with firms that deliver high revenue growth, with low/negative profits and negative free cash flows being treated as young firms. It is more data-intensive, since making a judgment on what comprises high (revenue growth or margins) requires estimating these metrics across all firms.

While I delve into the details of all three measures, corporate age works surprisingly well as a proxy for where a company falls in the life cycle, as can be seen in this table of all publicly traded companies listed globally, broken down by corporate age into ten deciles:

As you can see, the youngest companies have much higher revenue growth and more negative operating margins than older companies.

    Ultimately, the life cycles for companies can vary on three dimensions - length (how long a business lasts), height (how much it can scale up before it plateaus) and slope (how quickly it can scale up). Even a cursory glance at the companies that surround you should tell you that there are wide variations across companies, on these dimensions. To see why, consider the factors that determine these life cycle dimensions:

Companies in capital-light businesses, where customers are willing to switch from the status quo, can scale up much faster than companies in capital-intensive businesses, where brand names and customer inertia can make breakthroughs more difficult. It is worth noting, though, that the forces that allow a business to scale up quickly often limit how long it can stay at the top and cause decline to be quicker, a trade off that was ignored during the last decade, where scaling up was given primacy.

    The drivers of the corporate life cycle can also explain why the typical twenty-first century company faces a compressed life cycle, relative to its twentieth century counterpart. In the manufacturing-centered twentieth century, it took decades for companies like GE and Ford to scale up, but they also stayed at the top for long periods, before declining over decades. The tech-centered economy that we live in is dominated by companies that can scale up quickly, but they have brief periods at the top and scale down just as fast. Yahoo! and BlackBerry soared from start ups to being worth tens of billions of dollars in a blink of an eye, had brief reigns at the top and melted down to nothing almost as quickly. 

Tech companies age in dog years, and the consequences for how we manage, value and invest in them are profound. In fact, I would argue that the lessons that we teach in business school and the processes that we use in analysis need adaptation for compressed life cycle companies, and while I don't have all the answers, the discussion about changing practices is a healthy one.

Corporate Finance across the Life Cycle

    Corporate finance, as a discipline, lays out the first principles that govern how to run a business, and with a focus on maximizing value, all decisions that a business makes can be categorized into investing (deciding what assets/projects to invest in), financing (choosing a mix of debt and equity, as well as debt type) and dividend decisions (determining how much, if any, cash to return to owners, and in what form).

While the first principles of corporate finance do not change as a company ages, the focus and estimation processes will shift, as shown in the picture below:

With young companies, where the bulk of the value lies in future growth, and earnings and cash flows are often negative, it is the investment decision that dominates; these companies cannot afford to borrow or pay dividends. With more mature companies, as investment opportunities become scarcer, at least relative to available capital, the focus not surprisingly shifts to financing mix, with a lower hurdle rate being the pay off. With declining businesses, facing shrinking revenues and margins, it is cash return or dividend policy that moves into the front seat. 

Valuation across the Life Cycle

    I am fascinated by valuation, and the link between the value of a business and its fundamentals - cash flows, growth and risk. I am also a realist and recognize that I live in a world, where pricing dominates, with what you pay for a company or asset being determined by what others are paying for similar companies and assets:

All companies can be both valued and priced, but the absence of history and high uncertainty about the future that characterizes young companies makes it more likely that pricing will dominate valuation more decisively than it does with more mature firms. 

    All businesses, no matter where they stand in the life cycle, can be valued, but there are key differences that can be off putting to some. A well done valuation is a bridge between stories and numbers, with the interplay determining how defensible the valuation is, but the balance between stories and numbers will shift, as you move through the life cycle:

With young companies, absent historical data on growth and profitability, it is your story for the company that will drive your numbers and value. As companies age, the numbers will become more important, as the stories you tell will be constrained by what you have been able to deliver in growth and margins. If your strength as an analyst or appraiser is in bounded story telling, you will be better served valuing young companies, whereas if you are a number-cruncher (comfortable with accounting ratios and elaborate spreadsheet models), you will find valuing mature companies to be your natural habitat. 

    The draw of pricing is strong even for those who claim to be believers in value, and pricing in its simplest form requires a standardized price (a multiple like price earnings or enterprise value to EBITDA) and a peer group. While the pricing process is the same for all companies, the pricing metrics you use and the peer groups that you compare them to will shift as companies age:

For pre-revenue and very young companies, the pricing metrics will standardize the price paid (by venture capitalists and other investors) to the number of users or subscribers that a company has or to the total market that its product is aimed at. As business models develop, and revenues come into play, you are likely to see a shift to revenue multiples, albeit often to estimated revenues in a future year (forward numbers). In the mature phase, you will see earnings multiples become more widely used, with equity versions (like PE) in peer groups where leverage is similar across companies, and enterprise value versions (EV to EBITDA) in peer groups, where leverage is different across companies. In decline, multiples of book value will become more common, with book value serving as a (poor) proxy for liquidation or break up value. In short, if you want to be open to investing in companies across the life cycle, it behooves you to become comfortable with different pricing ratios, since no one pricing multiple will work on all firms.

Investing across the Life Cycle

    In my class (and book) on investment philosophies, I start by noting that every investment philosophy is rooted in a belief about markets making (and correcting) mistakes, and that there is no one best philosophy for all investors. I use the investment process, starting with asset allocation, moving to stock/asset selection and ending with execution to show the range of views that investors bring to the game:    

Market timing, whether it be based on charts/technical indicators or fundamentals, is primarily focused  on the asset allocation phase of investing, with cheaper (based upon your market timing measures) asset classes being over weighted and more expensive asset classes being under weighted. Within the stock selection phase, there are a whole host of investment philosophies, often holding contradictory views of market behavior. Among stock traders, for instance, there are those who believe that markets learn slowly (and go with momentum) and those who believe that markets over react (and bet on reversals). On the investing side, you have the classic divide between value and growth investors, both claiming the high ground. I view the differences between these two groups through the prism of a financial balance sheet:

Value investors believe that the best investment bargains are in mature companies, where assets in place (investments already made) are being underpriced by the market, whereas growth investors build their investment theses around the idea that it is growth assets where markets make mistakes. Finally, there are market players who try to make money from market frictions, by locking in market mispricing (with pure or near arbitrage). 

    Drawing on the earlier discussion of value versus price, you can classify market players into investors (who value companies, and try to buy them at a lower price, while hoping that the gap closes) and traders (who make them money on the pricing game, buying at a low price and selling at a higher one).  While investors and traders are part of the market in every company, you are likely to see the balance between the two groups shift as companies move through the life cycle:

Early in the life cycle, it is undeniable that traders dominate, and for investors in these companies, even if they are right in their value assessments, winning will require much longer time horizons and stronger stomachs. As companies mature, you are likely to see more investors become part of the game, with bargain hunters entering when the stock drops too much and short sellers more willing to counter when it goes up too much. In decline, as legal and restructuring challenges mount, and a company can have multiple securities (convertibles, bonds, warrants) trading on it, hedge funds and activists become bigger players.

    In sum, the investment philosophy you choose can lead you to over invest in companies in some phases of the life cycle, and while that by itself is not a problem, denying that this skew exists can become one. Thus, deep value investing, where you buy stocks that trade at low multiples of earnings and book value, will result in larger portions of the portfolio being invested in mature and declining companies. That portfolio will have the benefit of stability, but expecting it to contain ten-baggers and hundred-baggers is a reach. In contrast, a venture capital portfolio, invested almost entirely in very young companies, will have a large number of wipeouts, but it can still outperform, if it has a few large winners. Advice on concentrating your portfolio and having a margin of safety, both value investing nostrums, may work with the former but not with the latter.

Managing across the Life Cycle

    Management experts who teach at business schools and populate the premier consulting firms have much to gain by propagating the myth that there is a prototype for a great CEO. After all, it gives them a reason to charge nose-bleed prices for an MBA (to be imbued with these qualities) or for consulting advice, with the same end game. The truth is that there is no one-size-fits-all for a great CEO, since the qualities that you are looking for in top management will shift as companies age:

Early in the life cycle, you want a visionary at the top, since you have to get investors, employees and potential customers to buy into that vision. To turn the vision into products and services, though, you need a pragmatist, willing to accept compromises. As the focus shifts to business models, it is the business-building skills that make for a great CEO, allowing for scaling up and success. As a scaled-up business, the skill sets change again, with opportunism becoming the key quality, allowing the company to find new markets to grow in. In maturity, where playing defense becomes central, you want a top manager who can guard a company's competitive advantages fiercely. Finally, in decline, you want CEOs, unencumbered by ego or the desire to build empires, who are willing to preside over a shrinking business, with divestitures and cash returns high on the to-do list.

    There are very few people who have all of these skills, and it should come as no surprise that there can be a mismatch between a company and its CEO, either because they (CEO and company) age at different rates or because of hiring mistakes. Those mismatches can be catastrophic, if a headstrong CEO pushes ahead with actions that are unsuited to the company he or she is in charge off, but they can be benign, if the mismatched CEO can find a partner who can fill in for weaknesses:

While the possibilities of mismatches have always been part of business, the compression of corporate life cycles has made them both much more likely, as well as more damaging. After all, time took care of management transitions for long-lived twentieth century firms, but with firms that can scale up to become market cap giants in a decade, before scaling down and disappearing in the next one, you can very well see a founder/CEO go from being a hero in one phase to a zero in the next one. As we have allowed many of the most successful firms that have gone public in this century to skew the corporate finance game, with shares with different voting rights, we may be losing our power to change management at those firms where the need for change is greatest.

Aging gracefully? 

    The healthiest response to aging is acceptance, where a business accepts where it is in the life cycle, and behaves accordingly. Thus, a young firm that derives much of its value from future growth should not put that at risk by borrowing money or by buying back stock, just as a mature firm, where value comes from its existing assets and competitive advantages, should not risk that value by acquiring companies in new and unfamiliar businesses, in an attempt to return to its growth days. Acceptance is most difficult for declining firms, since the management and investors have to make peace with downsizing the firm. For these firms, it is worth emphasizing that acceptance does not imply passivity, a distorted and defeatist view of karma, where you do nothing in the face of decline, but requires actions that allow the firm to navigate the process with the least pain and most value to its stakeholders.

    It should come as no surprise that many firms, especially in decline, choose denial, where managers and investors come up with excuses for poor performance and lay blame on outside factors. On this path, declining firms will continue to act the way they did when they were mature or even growth companies, with large costs to everyone involved. When the promised turnaround does not ensue, desperation becomes the alternative path, with managers gambling large sums of other people’s money on long shots, with predictable results.

    The siren song that draws declining firms to make these attempts to recreate themselves, is the hope of a rebirth, and an ecosystem of bankers and consultants offers them magic potions (taking the form of proprietary acronyms that either restate the obvious or are built on foundations of made-up data) that will make them young again. They are aided and abetted by case studies of companies that found pathways to reincarnation (IBM in 1992, Apple in 2000 and Microsoft in 2013), with the added bonus that their CEOs were elevated to legendary status. While it is undeniable that companies do sometimes reincarnate, it is worth recognizing that they remain the exception rather than the rule, and while their top management deserves plaudits, luck played a key role as well.

    I am a skeptic on sustainability, at least as applied to companies, since its makes corporate survival the end game, sometimes with substantial costs for many stakeholders, as well as for society. Like the Egyptian Pharaohs who sought immortality by wrapping their bodies in bandages and being buried with their favorite possessions, companies that seek to live forever will become mummies (and sometimes zombies), sucking up resources that could be better used elsewhere.

In conclusion

    It is the dream, in every discipline, to come up with a theory or construct that explains everything in that disciple. Unlike the physical sciences, where that search is constrained by the laws of nature, the social sciences reflect more trial and error, with the unpredictability of human nature being the wild card. In finance, a discipline that started as an offshoot of economics in the 1950s, that search began with theory-based models, with portfolio theory and the CAPM, veered into data-based constructs (proxy models, factor analysis), and behavioral finance, with its marriage of finance and psychology. I am grateful for those contributions, but the corporate life cycle has offered me a low-tech, but surprisingly wide reaching, construct to explain much of what I see in business and investment behavior. 

    If you find yourself interested in the topic, you can try the book, and in the interests of making it accessible to a diverse reader base, I have tried to make it both modular and self-standing. Thus, if you are interested in how running a business changes, as it ages, you can focus on the four chapters that look at corporate finance implications, with the lead-in chapter providing you enough of a corporate finance foundation (even if you have never taken a corporate finance class) to be able to understand the investing, financing and dividend effects. If you are an appraiser or analyst, interested in valuing companies across the life cycle, it is the five chapters on valuation that may draw your interest, again with a lead-in chapter containing an introduction to valuation and pricing. As an investor, no matter what your investment philosophy, it is the four chapters on investing across the life cycle that may appeal to you the most. While I am sure that you will have no trouble finding the book, I have a list of book retailers listed below that you can use, if you choose, and the webpage supporting the book can be found here. 

    If you are budget-constrained or just don't like reading (and there is no shame in that), I have also created an online class, with twenty sessions of 25-35 minutes apiece, that delivers the material from the book. It includes exercises that you can use to check your understanding, and the link to the class is here. 

YouTube Video

Book and Class Webpages

1. Book webpage: https://pages.stern.nyu.edu/~adamodar//New_Home_Page/CLC.htm
2. Class webpage: https://pages.stern.nyu.edu/~adamodar//New_Home_Page/webcastCLC.htm
3. YouTube Playlist for class: https://www.youtube.com/playlist?list=PLUkh9m2BorqlpbJBd26UEawPHk0k9y04_

Links to booksellers

1. Amazon: https://www.amazon.com/Corporate-Lifecycle-Investment-Management-Implications/dp/0593545060
2. Barnes & Noble: https://www.barnesandnoble.com/w/the-corporate-life-cycle-aswath-damodaran/1143170651?ean=9780593545065
3. Bookshop.org: https://bookshop.org/p/books/the-corporate-lifecycle-business-investment-and-management-implications-aswath-damodaran/19850366?ean=9780593545065
4. Apple: https://books.apple.com/us/audiobook/the-corporate-life-cycle-business-investment/id1680865376

There is an Indian edition that will be released in September, which should be available in bookstores there. The Indian edition can be found on Amazon India.

1. Amazon India: https://www.amazon.in/Corporate-Life-Cycle-Investment-Implications/dp/0143471392

Country Risk: My 2024 Data Update

After the 2008 market crisis, I resolved that I would be far more organized in my assessments and updating of equity risk premiums, in the United States and abroad, as I looked at the damage that can be inflicted on intrinsic value by significant shifts in risk premiums, i.e., my definition of a crisis. That precipitated my practice of estimating implied equity risk premiums for the S&P 500, at the start of every month, and following up of using those estimated premiums when valuing companies during that month. The 2008 crisis also gave rise to two risk premium papers that I have updated each year: the first looks at equity risk premiums, what they measure, how they vary across time and how best to estimate them, with the last update in March 2024. The second focuses on country risk and how it varies across geographies, with the focus again on determinants, measures and estimation, which I update mid-year each year. This post reflects my most recent update from July 2024 of country risk, and while you can read the entire paper here, I thought I would give you a mildly abridged version in this post.

Country Risk: Determinants

    At the risk of stating the obvious, investing and operating in some countries is much riskier than investing and operating in others, with variations in risk on  multiple dimensions. In the section below, I highlight the differences on four major dimensions - political structure, exposure to war/violence, extent of corruption and protections for legal and property rights, with the focus firmly on the economic risks rather than on social consequences.

a. Political Structure

    Would you rather invest/operate in a democracy than in an autocracy? From a business risk perspective, I would argue that there is a trade off, sometimes making the former more risky than the latter, and sometimes less so. The nature of a democracy is that a government will be less able to promise or deliver long term predictable/stable tax and regulatory law, since losing an election can cause shifts in policy. Consequently, operating and investing in a democratic country will generally come with more risk on a continuous basis, with the risk increasing with partisanship in the country. Autocratic governments are in a better position to promise and deliver stable and predictable business environments, with two caveats. The first is that when change comes in autocracies, it will be both unexpected and large, with wrenching and discontinuous shifts in economic policy. The second is that the absence of checks and balance (legal, legislative, public opinion) will also mean that policy changes can be capricious, often driven by factors that have little to do with business or public welfare. 

    Any attempt to measure political freedom comes with qualifiers, since the biases of the measuring service on what freedoms to elevate and which ones to ignore will play a role, but in the figure below, I report the Economist's Democracy Index, which is based upon five measures - electoral process and pluralism, government functioning, political participation, democratic social culture and civil liberties:

Democracy Index in 2023: Source: The Economist

Based upon the Economist's democracy measures, much of the world remains skewed towards authoritarianism, changing the risk exposures that investors and businesses face when operating in those parts of the world. 

b. War and Violence

    Operating a business becomes much more difficult, when surrounded by war and violence, from both within and outside the country. That difficulty also translates into higher costs, with those businesses that can buy protection or insurance doing so, and those that cannot suffering from damage and lost revenues. Drawing again on an external service, the Institute for Economics and Peace measures exposure to war and violence with a global peace index (with higher scores indicating more propensity towards violence):

Global Peace Index 2024: Source: Institute for Economics & Peace

While Africa and large swaths of Asia are exposed to violence, and Northern Europe and Canada remain peaceful, businesses in much of the world (including the United States) remain exposed to violence, at least according to this measure.

c. Corruption

    As I have argued in prior posts, corruption operates as an implicit tax on businesses, with the tax revenues accruing to middlemen or third parties, rather than the government. 

Corruption Index 2023: Source: Transparency International

Again, while you can argue with the scores and the rankings, it remains undeniable that businesses in much of the world face corruption (and its associated costs). While there are some who attribute it to culture, I believe that the overriding reasons for corruption are systems that are built around licensing and regulatory constraints, with poorly paid bureaucrats operating as the overseers 

    There are other insidious consequences to corruption. First, as corruption becomes brazen, as it is in some parts of the world, there is evidence that companies operating in those settings are more likely to evade paying taxes to the government, thus redirecting tax revenues from the government to private players. Second, companies that are able and willing to play the corruption game will be put at an advantage over companies that are unable or unwilling to do so, creating a version of Gresham's law in businesses, where the least honorable businesses win out at the expense of the most honorable and honest ones. 

d. Legal and Property Rights

    When operating a business or making an investment, you are reliant on a legal system to back up your ownership rights, and to the extent that it does not do so, your business and investment will be worth less. The Property Rights Alliance, an entity that attempts to measure the strength of property rights, by country, measured property rights (physical and intellectual) around the world, to come up with a composite measure of these rights, with higher values translating into more rights. Their most recent update, from 2023, is captured in the picture below:

Property Rights Index 2023: Source: The Property Rights Alliance

Again, there are wide differences in property rights across the world; they are strongest in the North America and Europe and weakest in Africa and Latin America. Within each of these regions, though, there are variations across countries; within Latin America, Chile and Uruguay rank in the top quartile of countries with stronger property rights, but Venezuela and Bolivia are towards the bottom of the list. In assessing protections of property rights, it is worth noting that it is not only the laws that protect them that need to be looked at, but also the timeliness of legal action. A court that takes decades to act on violations of property rights is almost as bad as a court that does not enforce those rights at all.

    One manifestation of property right violation is nationalization, and here again there remain parts of the world, especially with natural resource businesses, where the risks of expropriation have increased. A Sustainalytics report that looked at metal miners documented 165 incidents of resources nationalization between 2017 and 2021, impacting 87 mining companies, with 22 extreme cases, where local governments ending contracts with foreign miners. Maplecroft, a risk management company, mapped out the trendline on nationalization risk in natural resources in the figure below:

Source: Maplecroft

National security is the reason that some governments use to justify public ownership of key resources. For instance, in 2022, Mexico created a state-owned company, Litio Para Mexico, to have a monopoly on lithium mining in the country, and announced a plan to renegotiate previously granted concessions to private companies to extract the resource.

Country Risk: External factors

    Looking at the last section, you would not be faulted for believing that country risk exposure is self-determined, and that countries can become less risky by working on reducing corruption, increasing  legal protections for property rights, making themselves safer and working on more predictable economic policies.  That is true, but there are three factors that are largely out of their control that can still drive country risk upwards.

1. Commodity Dependence

    Some countries are dependent upon a specific commodity, product or service for their economic success. That dependence can create additional risk for investors and businesses, since a drop in the commodity’s price or demand for the product/service can create severe economic pain that spreads well beyond the companies immediately affected. Thus, if a country derives 50% of its economic output from iron ore, a drop in the price of iron ore will cause pain not only for mining companies but also for retailers, restaurants and consumer product companies in the country. The United Nations Conference on Trade and Development (UNCTAD) measures the degree to which a country is dependent on commodities, by looking at the percentage of its export revenues come from a commodities, and the figure below captures their findings:

Proportion of revenues from commodities- 2019-2021; Source: UNCTAD

Why don’t countries that derive a disproportionate amount of their economy from a single source diversify their economies? That is easier said than done, for two reasons. First, while it is feasible for larger countries like Brazil, India, and China to try to broaden their economic bases, it is much more difficult for small countries like Peru or Angola to do the same. Like small companies, these small countries have to find a niche where they can specialize, and by definition, niches will lead to over dependence upon one or a few sources. Second, and this is especially the case with natural resource dependent countries, the wealth that can be created by exploiting the natural resource will usually be far greater than using resources elsewhere in the economy, which may explain the inability of economies in the Middle East to wean itself away from oil. 

II. Life Cycle dynamics

    As readers of this blog should be aware, I am fond of using the corporate life cycle structure to explain why companies behave (or misbehave) and how investment philosophies vary. At the risk of pushing that structure to its limits, I believe that countries also go through a life cycle, with different challenges and risks at each stage:

The link between life cycle and economic risk is worth emphasizing because it illustrates the limitations on the powers that countries have over their exposure to risk. A country that is still in the early stages of economic growth will generally have more risk exposure than a mature country, even if it is well governed and has a solid legal system. The old investment saying that gain usually comes with pain, also applies to operating and investing across the globe. While your risk averse side may lead you to direct your investments and operations to the safest parts of the world (say, Canada and Northern Europe), the highest growth is generally in the riskiest parts of the world.

3. Climate Change

    The globe is warming up, and no matter where you fall on the human versus nature debate, on causation, some countries are more exposed to global warming than others. That risk is not just to the health and wellbeing of those who live within the borders of these countries, but represents economic risks, manifesting as higher costs of maintaining day-to-day activity or less economic production.  To measure climate change, we turned to ResourceWatch, a global partnership of public, private and civil society organizations convened by the World Resources Institute. This institute measure climate change exposure with a climate risk index (CRI), measuring the extent to which countries have been affected by extreme weather events (meteorological, hydrological, and climatological), and their most recent measures (from 2021, with an update expected late in 2024) of global exposure to climate risk is in the figure below:

Climate Risk Index (CRI) in 2021: ResourceWatch

Note that higher scores on the index indicate more exposure to country risk, and much of Africa, Latin America and Asia are exposed. In fact, since this map was last updated in 2021, it is conceivable that climate risk exposure has increased across the globe and that even the green regions are at risk of slipping away into dangerous territory.

Country Life Cycle - Measures

    With that long lead in on the determinants of country risk, and the forces that can leave risk elevated, let us look at how best to measure country risk exposure. We will start with sovereign ratings, which are focused on country default risk, because they are the most widely used country risk proxies, before moving on to country risk scores, from public and private services, and closing with measures of risk premiums that equity investors in these countries should charge.

1. Sovereign Default Risk

    The ratings agencies that rate corporate bonds for default risk also rate countries, with sovereign ratings, with countries with higher (lower) perceived default risk receiving lower (higher) ratings. I know that ratings agencies are viewed with skepticism, and much of that skepticism is deserved, but it is undeniable that ratings and default risk are closely tied, especially over longer periods. The figure below summarizes sovereign ratings from Moody's in July 2024:

Moody's Sovereign Ratings in July 2024; Source: Moody's

If you compare these ratings to those that I reported in my last update, a year ago, you will notice that the ratings are stagnant for most countries, and when there is change, it is small. That remains my pet peeve with the rating agencies, which is not that they are biased or even wrong, but that they are slow to react to changes on the ground. For those searching for an alternative, there is the sovereign credit default swap (CDS) market, where you can market assessments of default risk. The figure below summarizes the spreads for the roughly 80 countries, where they are available:

Sovereign CDS Spreads on June 30, 2024: Source: Bloomberg

Sovereign CDS spreads reflect the pluses and minuses of a market-based measure, adjusting quickly to changes on the ground in a country, but sometimes overshooting as markets overreact. As you can see, the sovereign CDS market views India as safer than suggested by the ratings agencies, and for the first time, in my tracking, as safer than China (Sovereign CDS for India is 0.83% and for China is 1.05%, as of June 30, 2024).

2. Country Risk Scores

    Ubiquitous as sovereign ratings are, they represent a narrow measure of country risk, focused entirely on default risk. Thus, much of the Middle East looks safe, from a default risk perspective, but there are clearly political and economic risks that are not being captured. One antidote is to use a risk score that brings in these missed risks, and while there are many services that provide these scores, I use the ones supplied by Political Risk Services (PRS). PRS uses twenty two variables to measure country risk, whey then capture with a country risk score, from 0 to 100, with the riskiest countries having the lowest scores and the safest countries, the highest:

Country risk scores in July 2024: Political Risk Services

While I appreciate the effort that goes into these scores, I have issues with some of the scoring, as I am sure that you do. For instance, I find it incomprehensible that Libya and the United States share roughly the same PRS score, and that Saudi Arabia is safer than much of Europe. That said, I have tried other country risk scoring services (the Economist, The World Bank) and I find myself disagreeing with individual country scoring there as well.

3. Equity Risk Premiums

    Looking at operations and investing, through the eyes of equity investors, the risk that you care about is the equity risk premium, a composite measure that you then incorporate into expected returns. I don't claim to have prescience or even the best approach for estimating these equity risk premiums, but I have consistently followed the same approach for the last three decades. I start with the sovereign ratings, if available, and estimate default spreads based upon these ratings, and I then scale up these ratings for the fact that equities are riskier than government bonds. I then add these country risk premiums to my estimate of the implied equity risk premium for the S&P 500, to arrive at equity risk premiums, by country. 

For countries which have no sovereign ratings, I start with the country risk score from PRS for that country, find other (rated) countries with similar PRS scores, and extrapolate their ratings-based equity risk premiums. The final picture, at least as I see it in 2024, for equity risk premiums is below:

Download spreadsheet

You will undoubtedly disagree with the equity risk premiums that I attach to at least some of the countries on this list, and perhaps strongly disagree with my estimate for your native country, but you should perhaps take issue with Moody's or PRS, if that is so.

Country Risk in Decision Making

    At this point, your reaction to this discussion might be "so what?", since you may see little use for these concepts in practice, either as a business or as an investor. In this section, I will argue that understanding equity risk premiums, and how they vary across geographies, can be critical in both business and personal investing.

Country Risk in Business

    Most corporate finance classes and textbooks leave students with the proposition that the right hurdle rate to use in assessing business investments is the cost of capital, but create a host of confusion about what exactly that cost of capital measures. Contrary to popular wisdom, the cost of capital to use when assessing investment quality has little to do with the cost of raising financing for a company and more to do with coming up with an opportunity cost, i.e., a rate of return that the company can generate on investments of equivalent risk. Thus defined, you can see that the cost of capital that a company uses for an investment should reflect both the business risk as well as where in the world that investment is located. For a multinational consumer product company, such as Coca Cola, the cost of capital used to assess the quality of a Brazilian beverage project should be very different from the cost of capital estimated for a German beverage project, even if both are estimated in US dollars. The picture below captures the ingredients that go into a hurdle rate:

Thus, in computing costs of equity and capital for its Brazil and German projects, Coca Cola will be drawing on the equity risk premiums for Brazil (7.87%) and Germany (4.11%), leading to higher hurdle rates for the former.

    The implications for multi-business, multi-national companies is that there is no one corporate cost of capital that can be used in assessing investments, since it will vary both across businesses and across geographies. A company in five businesses and ten geographies, with have fifty different costs of capital, and while you complaint may that this is too complicated, ignoring it and using one corporate cost of capital will lead you to cross subsidization, with the safest businesses and geographies subsidizing the riskiest.

Country Risk in Investing

    As investors, we invest in companies, not projects, with those companies often having exposures in many countries. While it is possible to value a company in pieces, by valuing each its operations in each country, the absence of information at the country level often leads us to valuing the entire company, and when doing so, the risk exposure for that company comes from where it operates, not where it does business. Thus, when computing its cost of equity, you should look not only at its businesss risk, but what parts of the world it operates in:

In intrinsic valuation, this will imply that a company with more of its operations in risky countries will be worth less than a company with equivalent earnings, growth and cash flows with operations in safer countries. Thus, rather than look at where a company is incorporated and traded, we should be looking at where it operates, both in terms of production and revenues; Nvidia is a company incorporated and traded in the United States, but as a chip designed almost entirely dependent on TSMC for its chip manufacture, it is exposed to China risk.

    It is true that most investors price companies, rather than value them, and use pricing metrics (PE ratios, EV to EBITDA) to judge cheap or expensive. If our assessment of country risk hold, we should expect to see variations in these pricing metrics across geographies. We computed EV to EBITDA multiples, based upon aggregate enterprise value and EBITDA, by country, in July 2024, and the results are captured in the figure below:

Source: Raw data from S&P Capital IQ

The results are mixed. While some of the riskiest parts of the world trade at low multiples of EBITDA, a significant part of Europe also does, including France and Norway. In fact, India trades at the highest multiple of EBITDA of any country in the world, representing how growth expectations can trump risk concerns. 

Currency Effects

    You may find it odd that I have spent so much of this post talking about country risk, without bringing up currencies, but that was not an oversight. It is true that riskier countries often have more volatile currencies that depreciate over time, but this more a symptom of country risk, than a cause. As I will argue in this section, currency choice affects your growth, cash flow and discount rate estimates, but ultimately should have no effect on intrinsic value.    

    If you value a company in US dollars, rather than Indian rupees, should the numbers in your valuation be different? Of course, but the reason for the differences lies in the fact that different currencies bring different inflation expectations with them, and the key is to stay consistent:

If expected inflation is lower in US dollars than in rupees, the cost of capital that you should obtain for a company in US dollars will be lower than the cost of capital in rupees, with the difference reflecting the expected inflation differential. However, since your cash flows will also then have to be in US dollars, the expected growth that you should use should reflect the lower inflation rate in dollars, and if you stay consistent in your inflation estimates, the effects should cancel out. This is not just theory, but common sense. Currency is a measurement mechanism, and to claim that a company is undervalued in one currency (say, the rupee) while claiming that it is overvalued at the same time in another currency (say, the US dollar) makes no sense. To practitioners who will counter with examples, where the value is different, when you switch currencies, my response is that there is a currency view (that the rupee is under or over priced relative to the dollar) in your valuation in your valuation, and that view should not be bundled together with your company story in a valuation.

    As we noted in the last section, the place that currency enters your valuation is in the riskfree rate, and if my assertion about expected inflation is right, variations in riskfree rates can be attributed entirely to difference in expected inflation. At the start of July 2024, for instance, I estimated the riskfree rates in every currency, using the US treasury bond rate as my dollar riskfree rate, and the differential inflation between the currency in question and the US dollar:

My estimates are in the appendix to this post. In the same vein, inflation also enters into expected exchange rate calculations:

This is, of course, the purchasing power parity theorem, and while currencies can deviate from this in the short term, it remains the best way to ensure that your currency views do not hijack your valuation.

YouTube Video

My Country risk premium paper

1. Country Risk: Determinants, Measures and Implications - The 2024 Edition

My Data Links

1. Equity risk premiums, by country - July 2024 (also includes ratings, PRS scores and sovereign CDS)
2. Riskfree rates in currencies, based upon differential inflation - July 2024

External Data Links

1. Democracy Index 2023, The Economist
2. Global Peace Index 2024: Source: Institute for Economics & Peace
3. Corruption Index 2023, Transparency International
4. International Property Rights Index 2023, Institute for Property Rights
5. Climate Risk Index 2021, ResourceWatch