Economics

Why Low Returns Are Predicted for Stocks Over the Next Decade

Scott Buchanan2 Comments

I saw this scary-looking graphic of S&P 500 returns versus price/earnings (P/E) ratios a couple of days ago:

JPMorgan

The left-hand side shows that there is very little correlation between the current forward P/E ratio and the returns in the next year; as we have seen in the past few years, and canonically in say 1995-1999, market euphoria can commonly carry over from one year to the next. (See here for discussion of momentum effect in stock prices). So, on this basis, the current sky-high P/E should give us no concern about returns in the next year.

However, the right-hand side is sobering. It shows a very strong tendency for poor ten-year returns if the current P/E is high. In fact, this chart suggests a ten-year return of near zero, starting with the current market pricing. Various financial institutions are likewise forecasting a decade of muted returns [1].

The classic optimistic-but-naïve response to unwelcome facts like these is to argue, “But this time it’s different.” I am old enough to remember those claims circa 1999-2000 as P/E’s soared to ridiculous heights. Back then, it was “The internet will change EVERYTHING!”.  By that, the optimists meant that within a very few years, tech companies would find ways to make huge and ever-growing profits from the internet. Although the internet steadily became a more important part of life, the rapid, huge monetization did not happen, and so the stock market crashed in 2000 and took around ten years to recover.

A big reason for the lack of early monetization was the lack of exclusive “moats” around the early internet businesses. Pets.com was doomed from the start, because anyone could also slap together a competing site to sell dog food over the internet. The companies that are now reaping huge profits from the internet are those like Google and Meta (Facebook) and Amazon that have established quasi-monopolies in their niches.

The current mantra is, “Artificial intelligence will change EVERYTHING!” It is interesting to note that the same challenge to monetization is evident. ChatGPT cannot make a profit because customers are not willing to pay big for its chatbot, when there are multiple competing chatbots giving away their services for practically free. Again, no moat, at least at this level of AI. (If Zuck succeeds in developing agentic AI that can displace expensive software engineers, companies may pay Meta bigly for the glorious ability to lay off their employees).

My reaction to this dire ten-year prognostication is two-fold. First, I have a relatively high fraction of my portfolio in securities which simply pump out cash. I have written about these here and here. With these investments, I don’t much care what stock prices do, since I am not relying on some greater fool to pay me a higher price for my shares than I paid. All I care is that those dividends keep rolling in.

My other reaction is…this time it may be different (!), for the following reason: a huge fraction of the S&P 500 valuation is now occupied by the big tech companies. Unlike in 2000, these companies are actually making money, gobs of money, and more money every year. It is common, and indeed rational, to value (on a P/E basis) firms with growing profits more highly than firms with stagnant earnings. Yes, Nvidia has a really high P/E of 43, but its price to earnings-growth (PEG) ratio is about 1.2, which is actually pretty low for a growth company.

So, with a reasonable chunk of my portfolio, I will continue to party like it’s 1999.

[1] Here is a blurb from the Llama 3.1 chatbot offered for free in my Brave browser, summarizing the muted market outlook:

Financial institutions are forecasting lower stock market returns over the next decade compared to recent historical performance. According to Schwab’s 2025 Long-Term Capital Market Expectations, U.S. large cap equities are expected to deliver annualized returns of 6% over the next decade, while international developed market equities are projected to slightly outperform at 7.1%.1 However, Goldman Sachs predicts a more modest outlook, with the S&P 500 expected to return around 3% annually over the next decade, within a range of –1% and 7%.42 Vanguard’s forecasts also indicate a decline in expected returns, with U.S. equities falling to a range of 2.8% to 4.8% annually. These forecasts suggest that investors may face a period of lower returns compared to the past decade’s 13% annualized total return.

RGDP Underestimates Welfare

Zachary BartschLeave a comment

Like many Principles of Macroeconomics courses, mine begins with an introduction to GDP. We motivate RGDP as a measure of economic activity and NGDP as an indicator of income or total expenditures. But how does more RGDP imply that we are better off, even materially? One entirely appropriate answer is that the quantities of output are greater. Given some population, greater output means more final goods and services per person. So, our real income increases.  But what else can we say?

First, after adjusting for price changes, we can say that GDP underestimates the value that people place on goods and services that are transacted in markets. Given that 1) demand slopes down and 2) transactions are consensual, it stands to reason that everyone pays no more than their maximum value for things. This implies that people’s willingness to pay for goods surpasses their actual expenditures. Therefore, RGDP is a lower bound to the economic benefits that people enjoy. Without knowing the marginal value that people place on all quantities less than those that they actually buy, we have no idea how much more value is actually provided in our economy.

Continue reading →

Triumph of the Data Hoarders

James Bailey3 Comments

Several major datasets produced by the federal government went offline this week. Some, like the Behavioral Risk Factor Surveillance Survey and the American Community Survey, are now back online; probably most others will soon join them. But some datasets that the current administration considers too DEI-inflected could stay down indefinitely.

This serves as a reminder of the value of redundancy- keeping datasets on multiple sites as well as in local storage. Because you never really know when one site will go down- whether due to ideological changes, mistakes, natural disasters, or key personnel moving on.

External hard drives are an affordable option for anyone who wants to build up their own local data hoard going forward. The Open Science Foundation site allows you to upload datasets up to 50 GB to share publicly; that’s how I’ve been sharing cleaned-up versions of the BRFSS, state-levle NSDUH, National Health Expenditure Accounts, Statistics of US Business, and more. If you have a dataset that isn’t online anywhere, or one that you’ve cleaned or improved to the point it is better than the versions currently online, I encourage you to post it on OSF.

If you are currently looking for a federal dataset that got taken down, some good places to check are IPUMS, NBER, Archive.org, or my data page. PolicyMap has posted some of the federal datasets that seem particularly likely to stay down; if you know of other pages hosting federal datasets that have been taken down, please share them in the comments.

Was the US at Our Richest in the 1890s?

Jeremy Horpedahl2 Comments

Donald Trump has repeatedly said that the US was at our “richest” or “wealthiest” in the high-tariff period from 1870-1913, and sometimes he says more specifically in the 1890s. Is this true?

First, in terms of personal income or wealth, this is nowhere near true. I’ve looked at the purchasing power of wages in the 1890s in a prior post, and Ernie Tedeschi recently put together data on average wealth back to the 1880s. As you can probably guess, by these measures Trump is quite clearly wrong.

So what might he mean?

One possibility is tax revenue, since he often says this in the context of tariffs versus an income tax. Broadly this also can’t be true, as federal revenue was just about 3% of GDP in the 1890s, but is around 16% in recent years.

But perhaps it is true in a narrower sense, if we look at taxes collected relative to the country’s spending needs. Trump has referenced the “Great Tariff Debate of 1888” which he summarized as “the debate was: We didn’t know what to do with all of the money we were making. We were so rich.” Indeed, this characterization is not completely wrong. As economic historian and trade expert Doug Irwin has summarized the debate: “The two main political parties agreed that a significant reduction of the budget surplus was an urgent priority. The Republicans and the Democrats also agreed that a large expansion in government expenditures was undesirable.” The difference was just over how to reduce surpluses: do we lower or raise tariffs?

It does seem that in Trump’s mind being “rich” in this period was about budget surpluses. Let’s look at the data (I have truncated the y-axis so you can actually read it without the WW1 deficits distorting the picture, but they were huge: over 200% of revenues!):

It is certainly true that under parts of the high-tariff period, we did collect a lot of revenue from tariffs! In some years, federal surpluses were over 1% of GDP and 30% of revenues collected. But notice that this is not true during Trump’s favored decade, the 1890s. Following the McKinley Tariff of 1890, tariff revenue fell sharply (though probably not likely due to the tariff rates, but due to moving items like sugar to the duty-free list, as Irwin points out). The 1890s were not a decade of being “rich” with tariff revenue and surpluses.

Finally, also notice that during the 1920s the US once again had large budget surpluses. The income tax was still fairly new in the 1920s, but it raised around 40-50% of federal revenue during that decade. By the Trump standard, we (the US federal government) were once again “rich” in the 1920s — this is true even after the tax cuts of the 1920s, which eventually reduced the top rate to 25% from the high of 73% during WW1.

If we define a country as being “rich” when it runs large budget surpluses, the US was indeed rich by this standard in the 1870s and 1880s (though not the 1890s). But it was rich again by this standard in the 1920s. This is just a function of government revenue growing faster than government spending. And the growth of revenue during the 1870s and 1880s was largely driven by a rise in internal revenue — specifically, excise taxes on alcohol and tobacco (these taxes largely didn’t exist before the Civil War).

1890 was the last year of big surpluses in the nineteenth century, and in that year the federal government spent $318 million. Tariff revenue (customs) was just $230 million. There was only a surplus in that year because the federal government also collected $108 million of alcohol excise taxes and $34 million of tobacco excise taxes. In fact, throughout the period 1870-1899, tariff revenues are never enough to cover all of federal spending, though they do hit 80% in a few years (source: Historical Statistics of the US, Tables Ea584-587, Ea588-593, and Ea594-608):

One more thing: in some of these speeches, Trump blames the Great Depression on the switch from tariffs to income taxes. In addition to there really being no theory for why this would be the case, it just doesn’t line up with the facts. The 1890s were plagued by financial crises and recessions. The 1920s (the first decade of experience with the income tax) was a period of growth (a few short downturns) and as we saw above, large budget surpluses. The Great Depression had other causes.

After the Fall: What Next for Nvidia and AI, In the Light of DeepSeek

Scott BuchananLeave a comment

Anyone not living under a rock the last two weeks has heard of DeepSeek, the cheap Chinese knock-off of ChatGPT that was supposedly trained using much lower resources that most American Artificial Intelligence efforts have been using. The bearish narrative flowing from this is that AI users will be able to get along with far fewer of Nvidia’s expensive, powerful chips, and so Nvidia sales and profit margins will sag.

The stock market seems to be agreeing with this story. The Nvidia share price crashed with a mighty crash last Monday, and it has continued to trend downward since then, with plenty of zig-zags.

I am not an expert in this area, but have done a bit of reading. There seems to be an emerging consensus that DeepSeek got to where it got to largely by using what was already developed by ChatGPT and similar prior models. For this and other reasons, the claim for fantastic savings in model training has been largely discounted. DeepSeek did do a nice job making use of limited chip resources, but those advances will be incorporated into everyone else’s models now.

Concerns remain regarding built-in bias and censorship to support the Chinese communist government’s point of view, and regarding the safety of user data kept on servers in China. Even apart from nefarious purposes for collecting user data, ChatGPT has apparently been very sloppy in protecting user information:

Wiz Research has identified a publicly accessible ClickHouse database belonging to DeepSeek, which allows full control over database operations, including the ability to access internal data. The exposure includes over a million lines of log streams containing chat history, secret keys, backend details, and other highly sensitive information.

Shifting focus to Nvidia – – my take is that DeepSeek will have little impact on its sales. The bullish narrative is that the more efficient algos developed by DeepSeek will enable more players to enter the AI arena.

The big power users like Meta and Amazon and Google have moved beyond limited chatbots like ChatGPT or DeepSeek. They are aiming beyond “AI” to “AGI” (Artificial General Intelligence), that matches or surpasses human cognitive capabilities across a wide range of cognitive tasks. Zuck plans to replace mid-level software engineers at Meta with code-bots before the year is out.

For AGI they will still need gobs of high-end chips, and these companies show no signs of throttling back their efforts. Nvidia remains sold out through the end of 2025. I suspect that when the company reports earnings on Feb 26, it will continue to demonstrate high profits and project high earnings growth.

Its price to earnings is higher than its peers, but that appears to be justified by its earnings growth. For a growth stock, a key metric is price/earnings-growth (PEG), and by that standard, Nvidia looks downright cheap:

Source: Marc Gerstein on Seeking Alpha

How the fickle market will react to these realities, I have no idea.

The high volatility in the stock makes for high options premiums. I have been selling puts and covered calls to capture roughly 20% yields, at the expense of missing out on any rise in share price from here.

Disclaimer: Nothing here should be considered as advice to buy or sell any security.

Using Taylor Swift to teach about Adam Smith

Joy BuchananLeave a comment

It’s a niche thing, but Art Carden and I wrote a collection of Taylor Swift/Adam Smith essays. I’m going to use some for teaching this semester, so I wanted to post this in case it’s useful for other teachers.

In introductory economics courses, students often encounter Adam Smith as a one-dimensional figure – the patron saint of self-interest who wrote about the “invisible hand” of the market. But Smith was a far more nuanced thinker, and his insights about human nature remain relevant today. The challenge is making these complex ideas accessible to modern undergraduates.

That’s where this comes in as a teaching aid. Through three recent articles examining Swift’s very public decisions and artistic output, we can introduce students to Smith’s key ideas in a way that feels immediately relevant and engaging. From Swift’s struggles with public perception in “Anti-Hero” to the economic implications of her homemade cinnamon rolls, these pieces provide concrete, contemporary examples that illuminate Smith’s dual role as both moral philosopher and economic thinker. Many undergraduates are already familiar with Swift’s music and public persona, providing an accessible entry point to Smith’s more abstract concepts.

Here’s the recommended order to introduce our articles and a blurb on what you can learn (seriously).

“Anti-Hero as a Smithian Anthem” – This article introduces Smith’s foundational concept of the impartial spectator and his sophisticated view of human nature through a contemporary example. The article demonstrates that Smith wasn’t just an economist but a moral philosopher who understood how deeply humans care about others’ perceptions of them, showing students that economics isn’t just about money.

“Taylor Swift & The World’s Most Expensive Cinnamon Rolls” – This piece provides a bridge between Smith’s moral philosophy and his economic thinking, using opportunity cost analysis while simultaneously showing how rational economic actors might “inefficiently” spend time on activities that build social bonds. The article illustrates how Smith’s ideas about sympathy and social connection exist alongside, not in opposition to, his economic insights about specialization and efficiency.

“Would Adam Smith Tell Taylor Swift to Attend the Super Bowl?” – This article builds on the previous readings to explore the full complexity of Smith’s thought, showing how his ideas from both The Theory of Moral Sentiments and The Wealth of Nations can be applied to analyze real-world decisions.

How FRASER Enhances Economic Research and Analysis

Zachary BartschLeave a comment

Most of us know about FRED, the Federal Reserve Economic Data hosted by the Federal Reserve of St. Louis. It provides data and graphs at your fingertips. You can quickly grab a graph for a report or for a online argument. Of course, you can learn from it too. I’ve talked in the past about the Excel and Stata plugins.

But you may not know about the FRED FRASER. From their about page, “FRASER is a digital library of U.S. economic, financial, and banking history—particularly the history of the Federal Reserve System”. It’s a treasure trove of documents. Just as with any library, you’re not meant to read it all. But you can read some of it.

I can’t tell you how many times I’ve read a news story and lamented the lack of citations –  linked or unlinked.  Some journalists seem to do a google search or reddit dive and then summarize their journey. That’s sometimes helpful, but it often provides only surface level content and includes errors – much like AI. The better journalists at least talk to an expert. That is better, but authorities often repeat 2nd hand false claims too. Or, because no one has read the source material, they couch their language in unfalsifiable imprecision that merely implies a false claim.

A topical example would be the oft repeated blanket Trump-tariffs. That part is not up for dispute. Trump has been very clear about his desire for more and broader tariffs. Rather, economic news often refers back to the Smoot-Hawley tariffs of 1930 as an example of tariffs running amuck. While it is true that the 1930 tariffs applied to many items, they weren’t exactly a historical version of what Trump is currently proposing (though those details tend to change).

How do I know? Well, I looked. If you visit FRASER and search for “Smoot-Hawley”, then the tariff of 1930 is the first search result. It’s a congressional document, so it’s not an exciting read. But, you can see with your own eyes the diversity of duties that were placed on various imported goods. Since we often use the example of imported steel and since the foreign acquisition of US Steel was denied, let’s look at metals on page 20 of the 1930 act. But before we do, notice that we can link to particular pages of legislation and reports – nice! Reading the Smoot-Hawley Tariff Act’s original language, we can see the diverse duties on various metals. Here are a few:

Continue reading →

The Big Ideas

James Bailey2 Comments

Do I really think that the things I write about here and in my papers are the most important things in the world? No. Like most academics, I tend to emphasize the issues where I think I bring a unique perspective, rather than most important issues. But if you don’t realize this, you might get the impression that I think the things I normally talk about are the most important, rather than simply the most neglected and tractable / publishable. I don’t work on the most important issues because I see no good way for me to attack them- but if you do see a way, that is where you should focus. So what are the big issues of the 2020’s?

I see two issues that stand out above the many other important events of the day:

  • Artificial Intelligence: At minimum, the most important new technology in a generation; has the potential to bring about either utopia or dystopia. Do you have ideas for how to nudge it one way or another?
  • Rise of China: From extreme poverty to the world’s manufacturing powerhouse in two generations. What lessons should other countries learn from this for their own economic policy? How can we head off a world war and/or Chinese hegemony?

Focusing a bit more on economics, I see two perennial issues where there could be new opportunities to solve vital old questions:

  • Economic Development: We still don’t have a definitive answer to Adam Smith’s founding question of economics- why are some countries rich while other countries are poor, and how can the poor countries become rich? I think economic freedom is still an underrated answer, but even if you agree, the question remains of how to advance freedom in the face of entrenched interests who benefit from the status quo.
  • Robust Prediction: How can we make economics into something resembling a real science, one where predictions that include decimal places don’t deserve to be laughed at? Can you find a way to determine how much external validity an experiment has? Or how to use machine learning to get at causality? Or at least push existing empirical research to be more replicable?

I’ve added these points to my ideas page, since all this was inspired by me talking through the ideas on the page with my students and realizing how small and narrow they all seemed. Yes, small and narrow ideas are currently easier to publish in economics, but there is more to research and life than easy publications.

Forecasting 2025

James Bailey2 Comments

WSJ’s survey of economists reports that inflation expectations for 2025 were around 2% before the election, but are closer to 3% now. Their economists expect GDP growth slowing to 2%, unemployment ticking up slightly but staying in the low 4% range, with no recession. The basic message that 2025 will be a typical year for the US macroeconomy, but with inflation being slightly elevated, perhaps due to tariffs.

Kalshi has a lot of good markets up that give more detailed predictions for 2025:

For those who hope for DOGE to eliminate trillions in waste, or those who fear brutal austerity, the message from markets is that the huge deficits will continue, with the federal debt likely climbing to over $38 trillion by the end of the year. This is one reason markets see a 40% chance that the US credit rating gets downgraded this year.

While the US has only a 22% chance of a recession, China is currently at 48%, Britain at 80%, and Germany at 91%. The Fed probably cuts rates twice to around 4.0%.

Will wage growth keep pace with inflation? It’s a tossup. Corporate tax cuts are also a tossup. The top individual rate probably won’t fall below it’s current 37%.

If you want to make your own predictions for the year, but don’t want to risk money betting on Kalshi, there are several forecasting contests open that offer prizes with no risk:

ACX Forecasting Contest: $10,000 prize pool, 36 questions, must submit predictions by Jan 31st

Bridgewater Forecasting Contest: $25,000 prize pool, half of prizes are reserved for undergraduates. Register now to make predictions between Feb 3rd and March 31st. Doing well could get you a job interview at Bridgewater.