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

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

The Push and Pull of Data

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

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

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

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

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

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

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

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

(c) Crosscheck with alternate data providers, for consistency.

Data Coverage

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

    

Data Sources

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

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

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

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

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

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

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

   -   

Data Universe

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

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

Current data link

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

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

Current data link

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

Usage

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

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

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

The Damodaran Bot!

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

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

YouTube Video

Links to data

1. My data entry page
2. Current data for 2026
3. Archived data 
4. Data variables

Trillion Dollar Market Caps: Fairy Tale Pricing or Business Marvels?

     Stock markets have always rewarded winners with large capitalizations, and with each new threshold, the questions begin anew of whether animal spirits or fundamentals are driving the numbers. A few weeks ago, Nvidia seemed unstoppable as its market capitalization crested $5 trillion, and while markets have turned skeptical since, the core questions have not gone away, and the answers come from two extremes. At one end are the "realists”, who view themselves as rational, above the fray and entirely data-driven, who argue that there is no business model that can support a value this high, and that Nvidia is overvalued. At the other end are the “AI true believers”,  who believe that if the market the company is going after is big enough, and they see AI as such a market, the upper bounds on value are released, the sky is the limit. As someone who entered the Nvidia sweepstakes early (in 2018) and has held it through much of its magical run, while expressing reservations about its pricing running ahead of its value, especially in the last three years, I will try to thread the needle (unsuccessfully, I am sure) in this post. In fact, rather than try to convince you that the company is under or overvalued, which is really your judgment to make, I will offer a simple model to reverse engineer from any given market capitalization, the revenues and profitability thresholds you have to meet, and allow you to come to your own conclusions.

A History of Market Cap Thresholds

    In 1901, US Steel was created  when Andrew Carnegie and J.P. Morgan consolidated much of the US steel business, with an eye to monopolizing the steel business, and the company became the first global firm with a market capitalization of a billion dollars, a small number in today's terms, but a number that was three times larger than the Federal budget in that year. The twentieth century was a good one for the US economy and US stocks, and the thresholds for highest market cap rose along the way:

Note the long stretch between Microsoft hitting the half-a-trillion dollar market cap in 1999, as the dot com boom peaked, and Apple doubling that threshold in 2018. Note also the quickening of the pace, as Apple hit the $2 trillion and $3 trillion market capitalization thresholds in the next four years, and Nvidia continued the streak hitting $4 trillion in 2024 and $5 trillion in 2025.  

    The table does provide a starting point to discussing multiple themes about how the US economy and US equities have evolved over the last century. You can see the shift away from the smokestack economy to technology , in the companies hitting the thresholds, with US Steel and GM firmly in the old economy mode, Microsoft, Apple, and Nvidia representing the new economy, and GE, with its large financial service arm, operating as a bridge. Having been in markets for all of the thresholds breached since 1981, the debate about whether the company breaking through has risen too much in too short a time period has been a recurring one. 

1. Substance: To get a measure of operating substance, I looked at the revenues and net income in the year leading into the year in which each company broke through the threshold. As you can see, US Steel had revenues of $0.56 billion and net income of $0.13 billion in 1901, the year in which its market cap exceeded $1 billion. GM, at the time its market cap breached $10 billion, had revenues of $9.83 billion, on which it generated net income of $0.81 billion; if PE ratios are your pricing metric of choice, that would have translated into a PE ratio of 12.35. Between 2018 and 2022, as Apple's market cap tripled from $1 trillion to $3 trillion, its annual revenues increased by 72%, and its net profits almost doubled. Finally, coming to Nvidia, the surge in market cap to $4 trillion in 2024 and $5 trillion in 2025 has come on revenues and net income that are about a quarter of the size of Apple's revenues and net income.
2. Life cycle: Every company that climbed to the top of the market cap tables and hit a market cap threshold historically has had single-digit revenue growth in the year leading up, with two exceptions: Microsoft in 1999, which was coming off a 28% revenue growth rate in 1998, and Nvidia in both 2024 and 2025 coming off even higher growth rates. Using this revenue growth rate in conjunction with the ages of the companies involved, I think it is fair to conclude that there has been a shift across time, with the mature companies (older, lower growth) that were at the top of the list for much of the twentieth century to much younger companies with growth potential rising to the top in this one.
3. Investment returns: Looking at the returns in the years after these companies hit their market cap thresholds, the results are mixed. While buying Apple in 2018, 2020, or 2022 would have yielded winning returns, at least over the next year or two, buying Microsoft in 1999 would not. In some of these cases, extending the time horizon would have made a difference, for the positive with Microsoft and for the negative with GE.

From a rational perspective, you could argue that these thresholds (billion, half a billion, trillion, etc.) are arbitrary and that there is nothing gained by focusing on them, but in a post that I wrote in September 2018 on Apple and Microsoft becoming trillion-dollar companies, I argued that crossing these arbitrary thresholds can draw attention to the numbers, with the effects cutting both ways, drawing in investors who regret missing out on the rising market cap in the periods before (a positive) and causing existing investors to take a closer look at what they are getting in return (perhaps a negative).

Market Caps: Pathways to Intrinsic Value Break Even

    Debates about whether a company is worth what it is trading for, whether it be a billion, ten billion, a hundred billion, or a trillion, devolve into shouting matches of "he said, she said", with each side staking out divergent perspectives on value and name-calling the other. Having been on the receiving end of some of that abuse, I decided to take a different pathway to examining this question. Rather than wonder whether Nvidia is worth five trillion or Eli Lilly is worth a trillion, I framed the question in terms of how much Nvidia or Eli Lilly would have to generate in revenues to justify their market capitalizations. The reason for my focus on revenues is simple since it is relatively unaffected by accounting games and can be compared to the total market size to gain perspective.

    The tool that I plan to use to arrive at this breakeven revenue is intrinsic valuation, and I chose not to use the acronym "DCF" deliberately. A discounted cash flow valuation (DCF) sounds like an abstraction, with models driving discount rates and financial modeling driving cash flows. To me, a DCF is just a tool that allows you to assess how much you would pay for a business or  the equity in the business, given its capacity to generate cash flows for its owners. Since it is easy to get lost in the labyrinth of estimates over time, I will simplify my DCF by doing two things. First, since our discussion is about market capitalization, i.e., the market's estimate of the value of equity, I will stay with an equity version of the model, where I focus on the cash flows that equity investors can get from the business and discount these cash flows back at a rate of return that they would demand for investing in that equity. In its most general form, this is what an equity valuation yields:

To simplify the assessment further, I structured this model to value equity in a mature company, i.e., one growing at or below the nominal growth rate of the economy in the very long term and again for simplicity, assumed that it could do this forever. The value of equity in this mature, long-lasting firm can be written as follows:

To put this model into use, let's take the $5 trillion dollar market capitalization that Nvidia commanded a few weeks ago and assign the following general inputs:

1. Cost of equity: Every month, I estimate the implied cost of equity for the S&P 500, and that number is model-agnostic and driven by what investors are willing to pay for stocks, given their fears and hopes. At the start of November 2025, that number was about 8%, with higher required returns (9-12%) for riskier stocks and  lower expected returns (6-7%) for safer stocks.
2. Inflation rate: While inflation has come down from its 2022 highs, it has stayed stubbornly above 2%, which the Fed claims as its target, and it seems more realistic to assume that it will stay at 2.5%, which is consistent with the riskfree rate being about 4%.
3. Stable growth rate (nominal growth rate in the economy): This is a number that is in flux, as economists worry about recessions and economic growth, but since this is a long-term number that incorporates expected inflation, it seems reasonable to assume an expected nominal growth of 4% for the economy (about 1.5% real growth). 

The net profit margin for Nvidia in the most recent twelve months has been 53.01%, an exceptionally high number, and the return on equity it has earned, on average over the last five years, is about 64.44%. I know that these numbers will come under pressure over time, as competition for AI chips picks up, and Nvidia's biggest customers (and chip maker) push for their share of the spoils, but even if you assume that Nvidia can maintain these margins, the revenue that Nvidia would have to deliver to justify its value is $483.38 billion.

Since Nvidia is still growing and you may need to wait, as equity investors, to get your cash flows, this breakeven number will get larger, the longer you have to wait and the lower the cash yield that equity investors receive during the growth period. In fact, with Nvidia, if you assume that it will take five years for them to grow to steady state, and that equity investors will receive a cash yield (cash flow as a percent of market cap) of 2% a year, the estimated breakeven revenue increases to $677.97 billion. The table below maps out the effects of waiting on breakeven revenues for a range of cash yield:

Download breakeven revenue spreadsheet

If, as seems reasonable, you assume that net margins and return on equity will decrease over time, the revenues you would need to break even will expand:

Download breakeven revenue spreadsheet

In fact, if you are a low-margin company, with net margins of 5% (as is the case with even the very best-run discount retailers) and a more modest return on equity of 10%, you will need revenues of $8 trillion or more to be able to get to a market capitalization of $5 trillion. 

    This framework can be used to compute breakeven revenues at other firms, and in the table below, we do so for the twelve largest market cap companies in the world, at their market capitalizations on November 20, 2025:

Note that, for simplicity, I have used a 2% cash yield and 4% growth rate in perpetuity for all of these firms, and that the breakeven revenues reflect current net margins and returns on equity at each of these firms, but with that said, there is still value in looking at differences. To allow for this comparison, I forecast out breakeven revenues five years from now, and estimated the growth that each company would need over the five years to justify its current market cap. Not surprisingly, Aramco can get to its breakeven revenues in year 5 with almost no growth (0.59% growth rate) but Tesla needs to deliver revenue growth of 86.4% to break even. Broadcom, another company that has benefited from the market's zeal for AI, has the next highest cliff to climb  in terms of revenue growth. In fact, for all of the Mag Seven stocks, growth has to 15% or higher to breakeven, a challenge given their scale and size.  In dollar value terms, three companies will need to get to breakeven revenues that exceed one trillion by year 5 to breakeven, Apple, Amazon and Tesla, but the first two are already more than a third of the way to their breakeven targets, but Tesla has a long, long way to go.

From Breakeven Revenues to Investment Action

    While some are more comfortable replacing conventional intrinsic valuation, where you estimate value and compare it to price, with a breakeven assessment, the truth is that the two approaches are born out of the same intent.

The Economics of Breakeven Revenues

    The model that I used to compute breakeven revenues is a vastly simplified version of a full equity valuation model, but even in its simplified form, you can see the drivers of breakeven revenues.

1. Market Capitalization: Since we work back from market capitalization to estimate breakeven revenues, the larger the market capitalization, holding all else constant, the greater the breakeven revenues will be. Using just Nvidia as an example, the company has seen its market capitalization rise from less than $400 billion in 2021, to $1 trillion in 2023, $2 trillion and $3 trillion thresholds in 2024 and crossed the $4 trillion and $ 5 trillion market cap levels in 2025. As the market cap has risen, the breakeven revenues have increased from $200 billion at the $1 trillion mark to $600 billion at the current market cap.
2. Operating Profitability: There are two profitability metrics in the drivers, with net margins determining how much of the revenues a company can convert to profits and the return on equity driving the reinvestment needed to sustain growth. Higher profitability will allow a company to deliver a higher market capitalization, at any given level of revenues. One reason manufacturing firms like Tesla will need higher breakeven revenues than software firms is that the unit economics are not as favorable.
3. Interest rates and equity risk premiums: The level of interest rates and equity risk premiums determine the cost of equity for all company, with higher values for the latter pushing up the costs of equity for riskier companies higher, relative to safer companies.
4. Operating and leverage risk: The riskiness in a business will push its cost of equity higher, and a higher debt load (relative to market cap) will have the same effect. A higher cost of equity will raise the breakeven revenues needed to deliver the same market capitalization.

In sum, while the breakeven revenue that you need to justify a given market cap always increases as the market cap increases, its level and rate of rise will be governed by business economics.

The 3Ps: Possible, Plausible, and Probable

    Replacing a conventional intrinsic valuation with a breakeven revenue analysis still leaves open the final investment question of whether that breakeven revenue is a number that you are comfortable with, as an investor. To address this question, I will draw on a  structure that I use for intrinsic valuation, where I put my assessment through what I call the 3P test.

It is possible that once you compute the breakeven revenues for a firm and measure it up against reality that it is impossible, i.e., a fairy tale. The most obvious case is when the breakeven revenues that you compute for your firm exceeds the total market for the products or services that it provides. If there is a lesson that tech companies learned in the last decade, it was in making the total addressable market (TAM) for their market into almost an art form, adding zeros and converting billion dollar markets into trillion dollar TAMs.  If you pass the "it is possible" test, you enter the plausibility zone, and nuance and business economics enter the picture more fully. Thus, assuming that a luxury retailer with sky-high margins and small revenues, by staying with a niche market, can increase its revenues ten-fold, while keeping margins intact, is implausible, as is a net margin of 40% in stable growth for a company with gross margins that are barely above that number. Finally, assuming that revenues can multiply over time, without reinvesting in acquisitions or projects to deliver those revenues are also pushing the boundaries of what is plausible. Once breakeven revenues pass the possible and plausible tests, you should be on more familiar ground as you look at the entire story line for the company, and assess whether the combination of growth, profitability and reinvestment that you are assuming with your story has a reasonable probability of being delivered.

    To apply these tests, consider Nvidia and Tesla. Nvidia needs about $590 billion in revenues by 2030 to break even at its current market capitalization of $4.3 trillion, requiring a growth rate in revenues of about 26% for the next five years. While that is a reach, it is both possible and plausible, with continued growth in the AI chip market and a dominant market share for Nvidia providing the pathway. It is on the probable test that you run into headwinds, since competition is heating up, and that will put pressure on both growth and margins. The problem for Tesla is that if the net margin stays low (at 5.31%), the revenues needed to breakeven exceed $2.2 trillion, and even with robotics and automated driving thrown into the business mix, you are pushing the limits of possibility. A Tesla optimist, though, would argue that these new businesses, when they arrive, will bring much higher net margins, which, in turn, will push down breakeven revenues and bring it into plausible territory. 

The Aggregated 3P Test - Big Market Delusion

    We tend to ask the 3P question at the company level with the companies that we choose to invest in (and like), but as we construct what look like plausible and probable stories for these companies, and invest in them accordingly, there are other investors are asking the same questions about the companies that they invest in, many of which compete in the same business as yours. That may sound unexceptional to you, but when the market that these companies are competing in is very large and still in formation, you can end up with what I described almost a decade ago as the big market delusion. In a paper on the topic, I used the dot.com boom, the cannabis stock surge and online advertising as case studies to explain how this behavior is a feature of big markets

The AI storyline clearly fits the big market delusion. There is talk of a "huge" market for AI products and services, with little to show as tangible evidence of that market’s existence right now, and that potential has drawn massive investments in AI architecture from tech companies. Along the way investors have also fallen under the spell of the big market, and have pushed up the market capitalizations of almost every company in the space. Using the language of breakeven revenues, investors in each of these companies is attributing large breakeven revenues to their chosen companies, but the delusion comes from the reality that if you aggregated these breakeven revenues across companies, the market is not big enough to sustain all of them. In short, each company passed the possible and plausible test, but in the aggregate, you are chasing an impossible target.

    While the big market delusion is at play in every aspect of AI, one segment where it is most visible right now is in the Large Language Models (LLM) space, where high profile players like ChatGPT, Gemini, Grok and Claude are vying for users, and their creators are being rewarded with nosebleed pricing. OpenAI, while still unlisted, has used the early lead that ChatGPT gave it in the LLM race to attract investments from a host of big tech companies (including Nvidia and Amazon) and venture capitalists, with the most recent investors pricing it at $500 billion, an astonishing number, given that the company reported revenues of only $13 billion in the most recent twelve months. Anthropic, the creator of Claude, has seen its pricing jump in the most recent funding round (from Microsoft and Nvidia in November 2025) to $350 billion, fifty times its revenues of $7 billion in the last twelve months. Elon Musk's owners stake in xAI, Grok's originator, was estimated to be worth $230 billion in November 2025, again an immense multiple of its revenues of $3.2 billion (if you include combined revenues with X). Expanding the list to the large tech companies, it is undeniable that some of Alphabet's massive rise in market capitalization in 2025 is because of its ownership of Gemini, and that Meta (with Llama) and Amazon (with Nova) have also seen bumps in market capitalization. Finally, while Deepseek is no longer making headlines, it is also in the space, competing for business. In the aggregate, LLM ownership is being priced at $1.5 trillion or more, and the collective revenues, even generously defined, are less than $100 billion. It is entirely plausible that a big market exists for LLMs, and that one or even two of the players in this space will be winners, but in the aggregate, the market is overreaching.

The Management Effect

     The mechanics of the breakeven revenue process may make it seem like managers are bystanders in the process and that investing can be on autopilot, but they are not. In fact, when market capitalizations rise, and breakeven revenues run well ahead of current revenues, I would argue that management matters more than ever. Going back to the breakeven revenues that we computed for the twelve largest market cap companies in the world, I would make the case that management matters much less (if at all) in Aramco and Berkshire Hathway, where breakeven revenues are close to current revenues, and the investments needed to deliver those revenues have already been made, that at the companies that still have steep climbs ahead of them to get to breakeven revenues.

    In this context, I will reemphasize a concern that I raised at the height of Meta's metaverse investing fiasco, which is that investors at many tech companies, including most on the large cap list, have given up their corporate governance rights, often voluntarily (through the acceptance of shares with different voting rights), to founders and top management in these companies. When traditional corporate governance mechanisms break down, and top managers have unchecked power, there is an increased risk of overreach. That concern is multiplied in the LLM space, where Sam Altman (at OpenAI) and Elon Musk (at xAI) are more emperors than CEOs.

The Investing Bottomline

    I started this post with mentions of market cap thresholds being breached, as the market pricing pushes up into the trillions for some of the biggest stock market winners. But what are the implications for investors? 

1. Highly priced ≠ Overpriced: If you are an investor who considers any highly priced company to be overvalued, I hope that this post leads you to reconsider. By reframing a pricing in terms of breakeven revenues, profitability and reinvestment, it allows you to consider whether a stock, even if priced at $4 trillion, may still be a good buy. 
2. The 3P test: Once you compute the operating metrics you need to breakeven on an investment in a highly priced company, passing those metrics through the 3P test (Is it possible? Is it plausible? Is it probable?) allows you to examine each company on its merits and potential, rather than use a broad brush or a rule of thumb (based on PE ratios or revenue multiples).
3. Room to disagree: I have never understood why, even if you believe strongly that a stock is over or under priced, that you need to evangelize that belief or contest people with alternate views. I think that the pathway that you would need (in terms of revenue growth and profitability) to justify Nvidia's and OpenAI's current pricing is improbable, but that is just my view, and it is entirely possible that you have an alternate perspective, leading to the conclusion that they are undervalued.
4. Reality checks: No matter what your view, optimistic or pessimistic, you have to be open to changing your mind, as you are faced with data. Thus, if you have priced a company to deliver 20% growth in revenues over the next five years (to break even) and actual revenues growth comes in at 10%, you have to be willing to revisit your story, admit that you were wrong, and adapt. 

If you came into this post, expecting a definitive answer on whether Nvidia is overpriced, you are probably disappointed, but I hope that you use the breakeven spreadsheet to good effect to make up your own mind.

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Spreadsheets

1. Breakeven Revenue, given market capitalization

A Golden Year (2025): Gold's Price Surge - The Signal in the Noise!

    I grew up in India in a time where if you had wealth, your investment options were limited. A stock market with sparse listings, accompanied by a lack of trust in financial assets, led investors to put their wealth into tangible assets. Real estate was the most common choice but gold was a strong competitor, though investments in the latter often took the form of jewelry and ornaments. As financial markets have gained dominance across the globe, especially so in the last four decades, gold has retreated to the background, with lagging returns in most years. In 2025, as stock and bond markets climbed walls of worry almost nonchalantly to reach new highs, gold has also been a surprisingly big winner, building on a recovery that started in 2022 to crest $4000 an ounce in October 2025. For long term proponents of investing in gold, this has been vindication, but even for investors who have never held gold in their portfolios, there is a message from the gold's rise that they ignore at their own peril. I must confess that I have never felt the draw of gold, and have never held it in my portfolio, but I have always been fascinated by the hold that gold has on some investors, and the reasons for its longevity. In this post, I will start by first positioning gold in the investment continuum and then examining its price movements, both in 2025 and with a longer term perspective, to get a handle on the drivers of these movements, before looking at how gold may fit in investment portfolios. 

Gold: Commodity, Currency or Collectible?

    I have argued that all investments can be classified into one of four groups - assets, with expected cashflows, either contractual (fixed income) or residual, commodities, which derive their value from use as inputs into production of other products or services, currencies, used as mediums of exchange and stores of value, and collectibles, held for their scarcity and enduring demand. This categorization matters because it provides a starting point for discussions of how to attach prices to each:

With assets, you can estimate value based on expected cash flows and risk. but you also price them based upon demand and supply. With commodities like oil or iron ore, you may be able to estimate value, based upon aggregated demand and supply, but it is far more likely that pricing will dominate. With currencies and collectibles, the absence of expected cash flows makes pricing the only option, making mood and momentumkey variables determining pricing direction.

    To assess gold as an investment, we need to first start by classifying it and while it is not an asset, it can or has been a currency, a commodity and a collectible at different points in history and in different forms. 

• It is an inefficient currency, and while there are undoubtedly transactions where gold coins have been used as tender, difficulties associated with checking authenticity, security and breaking down into small units have limited its use through history. 
• It can be used as a commodity, as is the case when it is used to make jewelry or statues (or in tooth fillings), but even when used in this context, it is often held more for its value as a collectible than for aesthetic reasons. 
• It is as a collectible that gold has stood out, with governments, banks and individuals attaching value to it over time.

Thus, it is safe to say that it is gold's role as a collectible that has driven its pricing over time. To the question of "so what", there are implications that follow almost immediately, and that will animate our discussion of gold's performance in 2025:

• Since gold, absent cash flows, cannot be valued, arguing whether gold is under or over valued is a pointless one, just as it is for bitcoin. In fact, if your investment philosophy is strictly tethered to finding investments that are under valued by the market, gold will not have a place in your portfolio, explaining Warren Buffett's long standing aversion to it, as an investment. It is worth noting that Berkshire Hathaway did invest in Barrick Gold, but an investment in a gold mining company has expected cash flows and is thus an asset.
• Gold is priced every day, and that pricing process is driven by demand and supply, and while we will outline macro variables that can affect one or both, it is ultimately a process where mood and momentum will carry the day. 
  

Without doubt, gold is one of the longest standing collectibles, predating and outliving its competitors. So, what is it that explains gold's durability as a collectible? The following factors come into play, and in the process of assessing them, we can get some insight into gold's enduring standing:

1. Scarcity: The supply of gold is not fixed, since more gold can be extracted, but it is finite. At the start of 2025, there were approximately 244,000 metric tons of gold in the world, held in a variety of forms (jewelry, gold bars & coins etc.). While gold production in 2024 amounted added 3,000 tons to this quantity, it is estimated that that there about 60,000 metric tons of gold that are still in reserves. That puts it in a sweet spot between elements like platinum that are too scarce (about 10,000 metric tons) to be widely held, and more difficult to extract, and elements that are too plentiful to hold their value. 
2. Durability: For a collectible to hold its value, it has to be durable, and one of the reasons that gold acquired its collectible status is because it is chemically stable, malleable and does not oxidize or corrode (when it comes into contact with acids and other agents). 
3. Desirability: There is something about gold that exerts a hold on human beings. From the Greek myth of Midas, the king whose touch turned everything to gold, to the legend of El Dorado, a city made of gold, that led the Spanish to cross the ocean to seek it out in South America, gold has driven narratives and altered history. 

Clearly, gold is not the only collectible, but almost every collectible, starting with other precious metals, moving to fine art and even Pokemon cards can be assessed on these three dimensions.

Gold: A Pricing Perspective

    Gold has a long history in investing, and the best to way to understand where we are right now is to look back at that history. As you look back at up and down years, we can start to make sense of the fundamentals that drive gold prices, as well as the noise added by sentiment and momentum to the pricing process.    

A Usage History

      Gold has been viewed as precious by civilizations going back millennia, with evidence of usage in the form of coins going back to the Lydian civilization, located in Turkey in 600 BC, with the Greeks and the Romans following. In South America, where gold was abundant, it was more likely to have ceremonial or spiritual value, crafted into ornaments, ritual objects and artifacts, and it was only after the Spanish conquistadors arrived that gold acquired monetary status. In Asia, gold coins can be traced back to the Qin dynasty in China in 2500 BC, and to India and South East Asia.

    It is worth noting that for centuries, the issuers (governments and kingdom) of fiat currencies tied them to gold to get skeptical populaces to hold them. In the eighteenth century, this linkage was formalized in the gold standard, where paper currency issuance was backed by holdings in gold, with paper money convertible into gold.  England adopted a de facto bimetallic (silver and gold) standard in the early 1700s, but a miscalculation by Isaac Newton on the silver/gold ratio, where silver was overpriced relative to gold, made it a gold standard. While England did not formally adopt the gold standard until 1818, the United States, at its birth as a country, and eager to have its new currency (the dollar) be accepted, followed England’s model, with a brief break during the civil war in the 1860s. 

    In the second half of the nineteenth century, the gold standard became the base for most major currencies, but two events in the early twentieth century put it to the test. During the First World War, governments in need of money to fund their armies found their hands tied by the constraints of gold, and many were forced to abandon convertibility and the gold standard. The United States stayed with the gold standard into the Great Depression, with some economists blaming the Fed’s actions trying to defend it for worsening the economic collapse. In the face of crisis, individuals rushed to convert dollars to gold, leading to the halting of convertibility and an effective end to a true Gold Standard. After the Second World War, the United States emerged as the economic superpower, and with the Bretton-Woods agreement, the US dollar took the place of gold at the center of the global monetary system, with the dollar convertible to gold at a fixed price. That system held until the early seventies, but broke down as the dollar deflated, and in 1971, it was officially abandoned. While central banks continue to hold gold, the gold standard is now dead, though there are some who seek a return to the system, with its enforced discipline and rigidity.

A Pricing History

    As we noted at the start of this post, gold has had quite a run in 2025, as you can see in the chart below, where we traces it daily price movements during the year:

Through October 24, 2025, gold prices are up 57% for the year, posting significant increases every quarter of the year. To provide perspective on how this year measures up against history, we looked at the percentage change in gold prices every year going back to 1963.

Gold has had its ups and downs over time, with a surge in prices in the late 1970s, with the very best and very worst years in terms of returns occurring within two years of each other; gold prices were up 133% in 1979 and down 32.15% in 1981. Inflation was the culprit, and while we will take a closer look at it in the next section, we also computed the gold price in inflation-adjusted terms in the graph, and on October 24, 2025, that inflation-adjusted price also hit an all time high, using year-end prices. In the graph, you will notice that the gold price was stagnant before 1971, largely because of the convertibility of US dollars into gold. After the Bretton Woods agreement established the US dollar as the international reserve currency, the United States agreed to back it up by agreeing to convert US dollars at $35 an ounce, andgold prices (at least in dollar terms) stayed tethered to that price. In 1971, the United States abandoned that backing, and gold prices have been set by demand and supply since.

Drivers of gold prices

    There is a route that can be used to estimate the "fundamental" value of a commodity by gauging the demand for the commodity (based on its uses) and the supply. While that may work, at least in principle, for industrial commodities, it is tough to put into practice with precious metals in general, and gold because the demand is not driven primarily by practical uses. While gold does not have an intrinsic value, there are at least three factors historically that have influenced the price of gold- inflation, fear of crises and real interest rates.

1. Inflation

    If as is commonly argued, gold is an alternative to paper currency, the price of gold will be determined by how much trust individuals have in paper currency. Thus, it is widely believed that if the value of paper currency is debased by inflation, gold will gain in value. To see if the widely held view of gold as a hedge against inflation has a basis, we looked at changes in gold prices and the inflation rate each year from 1963-2024 in the figure below:

The co-movement of gold and inflation is strongest in the 1970s, a decade where the US economy was plagued by high inflation and the correlation between gold prices and the inflation rate is brought home, when you regress returns on gold against the inflation rate for the entire period:

% Change in Gold price = -0.06 + 3.92 (Inflation rate)  R squared = 18.8%        

While this regression does back the conventional view of gold as an inflation hedge, there are two potential weak spots. 

• The first is that the R-squared is only 19%, suggesting that factors other than inflation have a significant effect on gold prices. 
• The second is that removing the 1970s essentially removes much of the significance from this regression. In fact, while the large move in gold prices in the 1970s can be explained by unexpectedly high inflation during the decade, the rise of gold prices between 2001 and 2012 cannot be attributed to inflation. 

To get a cleaner look at the interaction between gold and inflation, we looked at the percentage change in gold prices, by decade, and contrasted it with the returns on stocks, bills, bonds and real estate in the table below:

Gold has three standout decades - 1971-1980, 2001-2010 and the last five years (2021-2025), with unexpectedly high inflation being the driver of returns in the first and third instances. Gold's surge in the 2001 to 2010 time period can be attributed partially to the 2008 crisis, but gold had several good years leading into the crisis. If there is one finding that we can glean from this data, gold is more a hedge against extreme (and unexpected) movements in inflation and does not really provide much protection against smaller inflation changes.

2. Fear of Crises

    Through the centuries, gold has been the safe haven for investors fleeing a crisis. Thus, as investor fears ebb and flow, gold prices should go up and down. To test this effect, we used two forward-looking measures of investor fears – the default spread on a Baa-rated bond and the implied equity risk premium (which is a forward looking premium, computed based upon stock prices and expected cash flows). As investor fears increase, you should expect to see these risk premiums in both the equity and the bond market increase, and gold to rise in concurrence. The figure below summarizes the risk premiums in financial markets (bond default spreads and equity risk premiums) and gold returns each year from 1963 to 2024:

While the relationship is harder to decipher than the one with inflation, higher equity risk premiums correlate with higher gold prices, but only barely. Again, regressing annual returns on gold against these two measures separately, we get:

% Change in Gold Price = -0.13 + 5.21 (ERP)                             R squared = 5.02%

% Change in Gold Price = 0.13 -1.32 (Baa Rate - T.Bond Rate)     R squared = 0.20%

These regressions suggest little or no relationship between bond default spreads and gold prices, but a modest positive relationship, albeit one with substantial noise, between gold prices and equity risk premiums. Thus, gold prices seem to move more with fear in the equity markets than with concerns in the bond market, with every 1% increase in the equity risk premium translating into an increase of 5.21% in gold prices. As with inflation, though, gold's protective role in crises seems to be greatest during potentially catastrophic economic events, giving it the patina as a crisis hedge.

3. Real interest rates

    One of the costs of holding gold is that while you hold it, you lose the return you could have made investing it in a financial asset, dividends on stocks and coupons on bonds. The magnitude of this opportunity cost is captured by the real interest rate, with higher real interest rates translating into much higher opportunity costs and thus lower prices for gold. The real interest rate can be measured directly used the inflation indexed treasury bond (TIPs) rate or indirectly by netting out the expected inflation from a nominal risk free (or close to risk free) rate. The figure below summarizes real interest rates and gold price changes on a year-by-year basis from 1963 to 2023:

Note that the TIPs rate is available only for the two decades and that the real interest rate is computed as the difference between the ten-year US treasury bond rate in that year and the realized inflation rate (rather than the expected inflation rate). Regressing changes in gold prices against the real interest rate yields the following:

% Change in Gold price = 0.18 – 4.69 (T.Bond Rate - Inflation Rate)        R Squared =21.9%

High real interest rates are negative for gold prices and low real interest rates, or negative real interest rates, push gold prices higher.

The Bottom line

    Gold is often touted as a hedge against inflation and crises, but the evidence from history is nuanced. With inflation, it is a better hedge against unexpected inflation than expected inflation, and even with unexpected inflation, only for increases that put inflation above normal bounds. In short, it is a hedge against hyper inflation. With crises as well, the evidence is mixed, since gold prices are, for the most part, unaffected by movements in equity and bond risk measures that fall within historical bounds, but increase during risk events that are uncommon and potentially catastrophic. Investors who add gold to their portfolios because of the protection it offers should recognize it more akin to buying insurance against extreme events, and more useful if the bulk of their wealth is in financial assets.

Is gold expensive, correctly priced or cheap?

    Knowing that gold prices move with inflation, equity risk premiums and real interest rates is useful, but it still does not help us answer the fundamental question of whether gold prices today are too high or low. Can you price gold against other investments or itself? The answer is yes, though the results are often noisy.

A. Against inflation

In companion papers, Erb and Harvey examined the relationship between gold prices and inflation. In these papers, the price of gold is related to the CPI index and a ratio of gold prices to the CPI index is computed. In the first of these papers, they argued that in the very long term, gold prices increase at roughly the inflation rate, but in the second, they do question that hypothesis. We try to replicate their findings and we use the US Department of Labor CPI index for all items (and all urban consumers) set to a base of 100 in 1982-84, but with data going back to 1947. The level of the index in December 2023 was 308.742. Dividing the gold price of $4118/oz on October 24, 2025, by the CPI index level of 324.80, on that day, yields a value of 17.81. To get a measure of whether that number is high or low, we computed it every year going back to 1963 in the figure below

The median value is 2.93 for the 1963-2024 period and 3.77 for the 1971-2024 period. Thus, based purely on the comparison of the current measure of the Gold/CPI ratio to the historical medians does miss the fact that lower interest rates and inflation in the last decade may be skewing the statistics. Consequently, we regressed the Gold/CPI index against equity risk premiums and real interest rates and while real interest rates seem to have little effect on the Gold/CPI ratio, there is strong evidence that it moves with the ERP, increasing (decreasing) as the ERP increases (decreases):

Gold Price/ CPI = -1.79 + 123.56 (ERP)              R Squared = 47.7%

The implied equity risk premium for the S&P 500 at the start of October 2025 was 4.03%, and plugging that value into the gold/CPI regression yields the following:

Gold/CPI (given ERP of 4.03% on 10/24/25) = -1.79+ 123.56 (.0403) = 3.19

Put simply, gold looks overpriced in October 2025, even after correcting for changing equity risk premiums.

B. Against other precious metals

There is another way that you can frame the relative value of gold and that is against other precious metals. For instance, you can price gold, relative to silver, and make a judgment on whether it is cheap or expensive (on a relative basis). At the end of October 2025, the gold price was $4118/oz and the silver price was $47.80/oz, yielding a ratio of 84.73 for gold to silver prices (4118/47.80). To get a measure of where this number stands in a historical context, we looked at the ratio of gold prices to silver prices from 1963 to 2025 in the figure below

The median value of 57.09 over the 1963-2024 period would suggest that gold is overpriced, relative to silver. Given that gold and silver move together more often than they move in opposite directions, we are not sure that this relationship can be mined to address the question of whether gold is fairly priced today, but it can still be the basis for trading across precious metals.

The Bottom Line

    The historical data yields two conclusions, albeit at odds with each other. If you believe that history is your best guide for the future and that mean reversion will win out, it is undeniable that gold is overpriced against almost every metric it is usually priced against. In fact, you could argue that the rise of gold prices in the last decade is unprecedented since it has not been accompanied by raging inflation or by big market crises (though there have been economic crises).  The counter is that using historical data as a guide, gold has been overpriced over the last decade, a period over which its price has increased almost four fold, from $1060/oz at the end of 2015 to $4,118 on October 24, 2025. When an investment stays overpriced for that long, it is legitimate to question whether the pricing metric is flawed, and whether there a structural shift has occurred that has shifted the distribution. In the case of gold, priced on demand and supply, that shift has to be almost entirely on the demand side, since the stock of gold has continued to expand at a slow, but steady pace, during the period, and here are some of the possible reasons:

1. More pathways to buying/holding gold: For centuries, extending into the last century, the only way to invest in gold was to hold it in its physical form, with all of the limitations on making fractional investments and the added transactions/storage costs. The rise of Gold ETFs has reduced or removed both constraints allowing more investors entree into the gold market.
2. Mistrust of central banks: Investments in financial assets (stocks and bonds) are a reflection of the trust  investors have in central banks and governments, working to preserve the buying power of the currencies that they issue. In the aftermath of central banking activism in the post-2008 period, that trust in central banks and governments has depleted, at least for a segment of the population, leading to a shift on their part to gold (and bitcoin).
3. Slippage of the US dollar: In the aftermath of Bretton Woods, the world adopted the US dollar as a global base currency, with a tether remaining to gold. During that period, central banks held gold, as backup for their currencies, though individuals were restricted or denied the ability to convert currency to gold. Even after the US removed its last formal connection to the gold standard in 1971, the strength of the dollar and the centrality of the US economy allowed investors to use the US dollar as a safe haven currency, as a substitute for gold. It is undeniable that the US economy and dollar have been under stress for the last decade or more, with the ratings downgrade for the US being only a manifestation of these stresses. With no other global currency ready (yet) to take the place of the dollar, you can argue that gold is once again asserting its role as safe haven, and that the rise in its price reflects that status.
4. The Trump effect: While the first two factors have been in play for decades, this year has seen unusual turmoil, as tariff threats and economic wars threaten to unravel an economic world order that has governed markets and economies for much of the last century. While there are some who will welcome that development, it is not clear what the replacement will be, and the possibility of a catastrophic outcome is perhaps greater than it was a year or two ago, and this too is a positive for gold prices.

For much of the last century, investors who held gold in their portfolios tended to be a subset of the market, older and more concerned about catastrophes than the rest of us, but it is undeniable that this group now is both larger and drawing in some who would have historically pushed it away. 

Investment Consequences

    With that long lead in, every investor is faced with the question of whether gold fits into their investment portfolios, and the reason for holding it. There are four pathways that an investor can follow with gold, and without any judgment attached, here they are, with the trade offs involved:

1. Gold as a core investment: There are some investors who have built their portfolios, with gold as a central component, representing a significant portion of their holdings.  
• The trade off: Looking at the last forty years of returns on different investment classes, you can see why making an argument for holding gold as your core investment is so difficult to justify. Gold, with its annual compounded return of 5.35% between 1984 and 2024, would have significantly underperformed an investment in US stocks, that earned a compounded return of 11.38%, a difference that translates into a significant shortfall in ending portfolio value for gold investors; investing in US stocks in 1984 would have generated almost ten times as high an ending value in 2024, as investing an equivalent amount in gold in 1984.
  
  
  
  
  
  In fact, gold has also been a more risky investment, on a stand alone basis, than stocks with a higher standard deviation in annual returns. Does that make gold investors irrational? Not necessarily, because they may define risk in terms of best case and worst case outcomes, and while stock prices, at least in their perspective, have no lower bound, gold has a lower bound value, at least based on history.
• The draw: For investors who have a deep attachment to gold combined with a distrust of financial assets, governments and central banks, the net effect of holding a portfolio dominated by gold is that it improves their odds of passing the sleep test, i.e., they don't lose sleep wondering how their portfolios are doing. In short, they are willing to accept lower compounded annual returns over the long term in return for the security of holding an investment that they view as timeless.
• The choices: Gold's standing comes from its long history as a collectible, but it is not the only collectible. Through time, investors have also put their money in precious gems and other metals (silver, platinum), art and collectibles. In fact, some of the rise in cryptos (currencies, tokens and assets) can be attributed to a subset of (mostly younger) investors, who share the distrust of governments and central banks with gold investors, deciding to use bitcoin as an alternative to gold.
2. Gold as insurance: For investors with the bulk of their portfolios in financial assets (stocks and bonds), gold holdings can help insure their portfolios, at least partially, against inflation and market/economic crises.
• The trade off: As we noted earlier in the post, gold has been only a weak hedge against inflation and market crises that fall within normal bounds, but has done much better as a edge against hyperinflation and catastrophic market/economic risks. Adding gold to a financial asset dominated portfolio can provide insurance against the latter, but only if held in large enough quantity to make a difference; given the history of stock and gold returns, a gold holding that is 5% of your portfolio will not be enough and you will need a holding closer to 15-20%.
• The draw: All investors should be concerned about catastrophic risks, but it is undeniable that this concern varies across investors, with older and more risk averse investors more inclined to have that concern. It is also true that worries about catastrophes vary over time, increasing across all investors in troubled times. 
• The choices: The rise of derivatives markets has increased the choices for investors to buy protection against hyperinflation and catastrophes. Thus, you can use ETFs and options to hedge your portfolio against market collapses, if that is your concern, or shift your investments to other currencies and countries, if your worry is about hyperinflation in the domestic currency.
3. Gold as a trade: In trading, the key to winning is timing, buying when prices are low and selling when they are high, and there are some who make their money on gold by timing its ups and downs well. 
• The trade off: Getting the timing right in trading is easier said that done. While the peaks and bottoms of gold prices are easy to pinpoint in hindsight, it is worth remembering that many investors who became rich riding the gold price boom from 1977-1979 lost it all in next five years. The traders who bought gold in 2022 are riding high, at the moment, after a three-year surge in gold prices, but they too may be looking at disappointment, if they do not cash out at the right time.
• The draw: Trading is a pricing game, and since price is determined more by mood and momentum, success in gold trading comes down to detecting momentum shifts before they occur, and trading on that basis. For some gold traders, this capacity may come from examining charts on gold prices and volume, and for others, it may be in reading the macroeconomic tealeaves, especially on inflation.
• The choices: If trading is your game, the market is ripe with targets, ranging from cryptos in the collective space to meme stocks, and many of these alternatives offer a bigger payoff to trading, since they are more volatile than gold and in some cases, offer more liquidity.
4. Gold as a signal: There are many investors who have no desire to own or or are averse to holding gold in their portfolios, but use gold prices as signals of either hyperinflation or economic catastrophes to structure their portfolios. 
• The trade off: The allure of gold as a signal of inflation and market crises comes from history, where gold prices have tended to rise during periods of high inflation and economic uncertainty. Much of the relationship, though, is contemporaneous, i.e,, gold prices rise in periods when inflation is high and risks surge, and there is only weak evidence of gold prices being a leading indicator of future changes. 
• The draw: Since portfolios composed primarily or entirely of financial assets are badly damaged by unexpected inflation or a market meltdown, having a predictor, even a flawed one, that can give advance warning has big payoffs. In particular, if gold prices rising is a signal that inflation will be higher than expected in the future, you could alter your asset allocation, shifting money from stocks and long terms bonds to short term bills and commercial paper, or even your asset selection, moving money from companies that have little pricing power and significant operating risk to companies with substantial pricing power and predictable earnings streams.
• The choices: Here again, markets offer other choices, with futures markets and forward contracts specifically targeted at predicting inflation or economic and market shocks. Thus, you could use inflation futures to protect against hyper inflation and volatility indicators (like the VIX) to hedge against market crises.

The Bottom Line

    Gold has had a good run this year, and I will not begrudge those who got into it early. Some undoubtedly just got lucky to be at the right place at the right time, but some were prescient in detecting a shift in the market vibe, especially in 2025. The truth is that the market for gold has been and always will be a niche market, drawing a subset of investors, but that niche shrinks and expands over time. When the world is stable and times are good, the niche is composed almost entirely of true believers, a mix of conspiracy theorists and doomsday cultists who believe that fiat currencies are more paper than money and that financial asset markets are designed to enrich insiders. In scarier times, the niche expands, drawing in investors who normally invest in stocks and bonds, but decide, either because of distrust of central banks or perceived market bubbles, that they need the safety of gold. While I do not have a ledger listing everyone holding gold in October 2025, I will wager that it includes names that you would normally expect to see in the list. After all, if Jamie Dimon and Ray Dalio actually mean what they say about markets being a bubble, would it not make sense for them to hold gold?

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Datasets

1. Gold, Bitcoin and Silver prices in 2025
2. Gold, Stocks, Bonds and Real Estate Returns: 1928-2025