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The stock market’s long-term record is one of the biggest reasons investors are told to stay invested. But that record hides a surprising reality: the typical individual stock has not delivered anything close to the market’s overall return.
In this episode, I’m joined by Hendrik “Hank” Bessembinder, whose research has reshaped how many investors think about stock market returns. Hank’s work shows that a relatively small number of extraordinary winners account for a huge share of the wealth created by public markets, which raises an important question for anyone trying to pick individual stocks: how confident should you be that you can identify those winners in advance?
We also discuss why averages can be misleading, how investors should think about the full range of possible outcomes rather than a single expected return, and why the way we measure investment success may not always match the way real people experience it. Toward the end of the conversation, we also explore whether AI could make stock market outcomes even more concentrated—or potentially push things in the opposite direction.
This is a conversation about diversification, humility, compounding, and the gap between what the market delivers and what most individual stocks actually do.
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Why the stock market creates wealth while most stocks do not (00:31)
I start by asking Hank about the paradox at the center of his research: the U.S. stock market has created enormous long-term wealth, yet most individual stocks have failed to beat Treasury bills.
Hank explains that this is the core finding. The stock market as a whole has been a powerful wealth-generating mechanism, but the typical individual stock has not delivered the market’s overall result. In fact, most stocks lose money over long horizons.
He also shares how he stumbled into this line of research. While working on a different study, he noticed that the average logarithmic return for a large sample of stocks was negative. That observation led him to dig deeper into the long-run performance of individual stocks, a research path he has now pursued for nearly a decade.
Skewness, shareholder wealth creation, and why a few stocks drive the market (03:37)
We talk about why the average individual stock return can look attractive while the median stock outcome is negative.
Hank explains skewness by comparing stock returns to a height distribution. In a normal, symmetric distribution, most observations cluster around the average. But long-term stock market outcomes do not look like that. Instead, most stocks produce poor results, while a few extreme winners are so large that they pull the average up.
That asymmetry is what allows two things to be true at once: the market does very well over time, while most individual stocks disappoint.
We also discuss the difference between buy-and-hold returns and shareholder wealth creation. A buy-and-hold return measures what happens if an investor buys a stock, reinvests dividends, and holds it. Shareholder wealth creation takes company size into account and looks at investors in aggregate, including share issuance, repurchases, and dividends.
Big winners, market concentration, and the case for diversification (11:13)
Hank explains that in his original study, a very small number of companies accounted for a large share of total shareholder wealth creation. In his more recent work covering 100 years of U.S. stock market history, that concentration has become even more striking.
In the original study, 90 firms accounted for half of net shareholder wealth creation. With only nine more years of data, that number fell to 46 firms. Hank attributes much of the recent increase in concentration to the outperformance of the largest companies since around 2013.
We talk about familiar names like Apple, Microsoft, Alphabet, Amazon, Nvidia, and Altria. Hank explains that Altria’s extraordinary long-term result came from compounding at a high—but not unimaginable—rate for a very long time. Nvidia, by contrast, has produced a much higher annualized return over a shorter period.
That leads to one of the practical lessons of the episode: it is extremely difficult to know in advance which stocks will become the great long-term winners. For most investors, Hank’s research reinforces the importance of broad diversification.
The challenge of identifying and holding the big winners (17:10)
I ask whether the main lesson from Hank’s work is that investors cannot reasonably expect to identify the biggest winners in advance.
Hank agrees that this is a major takeaway. Many investors want to know how to find the few stocks that will drive future market wealth creation, but the competition is intense. Even if markets are not perfectly efficient, many smart people are trying to identify the same opportunities.
We also discuss whether the bigger challenge is finding the winner, holding the winner, or not selling too early. Hank explains that there is no simple rule. Successful concentrated investing requires a combination of quantitative tools, valuation discipline, and strong intuition about business prospects.
He notes that some investors and firms may have the skill to run concentrated portfolios, but most investors should be humble about how hard that is.
Why Treasury bills, expected returns, and planning assumptions matter (25:01)
I ask Hank why he compares individual stocks to one-month Treasury bills in his research.
He explains that academics often focus on the risk premium: how much extra return investors receive for taking risk. Treasury bills serve as a proxy for cash or a low-risk alternative, making them a useful benchmark for assessing whether stocks compensated investors for the risk they took.
We then turn to financial planning and capital market assumptions. Hank cautions that relying on expected mean returns can be incomplete, especially when outcomes are skewed. A single expected return does not fully describe the range of possible investor experiences.
He argues that advisors and investors should talk more about probability distributions, median outcomes, and the full range of potential results rather than focusing only on a single average forecast.
Sustainable returns, sequence risk, and what investors are actually investing for (28:06)
Hank explains that most investors are not simply trying to maximize the size of their portfolio on the day they die. They invest because they want to withdraw money later—to fund retirement, support family, give to charity, or meet other life goals.
That is why he has explored alternative ways to measure long-term investment outcomes, including what he calls the sustainable return. The idea is to ask how much an investor could withdraw over time while preserving the real value of the original investment.
This leads to a discussion of sequence risk. Traditional return measures like arithmetic averages and buy-and-hold returns do not change if you rearrange the order of returns. But once an investor is adding or withdrawing money, the order of returns matters a lot.
Hank notes that advisors often understand sequence risk because they see it in retirement planning, but it has not been emphasized nearly as much in academic theory.
Arithmetic returns, geometric returns, and the problem with alpha (33:22)
We discuss why arithmetic average returns can be misleading when investors think about long-term compounding.
Hank explains that arithmetic returns are easy to calculate, but they do not map cleanly to an investor’s actual multi-period experience. Geometric returns are more closely tied to buy-and-hold outcomes because they reflect compounding.
Arithmetic returns are almost always higher than geometric returns when there is volatility. The difference is sometimes called volatility drag.
This matters because many familiar investment metrics—including alpha, Sharpe ratios, and mean-variance optimization—are based on arithmetic means. Hank points out that a strategy can show positive alpha while still underperforming a benchmark in terms of compounded long-term returns.
AI, market concentration, and the lessons investors still resist (37:17
I ask Hank whether artificial intelligence might accelerate the winner-take-all dynamics seen in stock markets.
Hank says he genuinely does not know. AI will likely change the world, but it is unclear who will capture the economic value. The biggest beneficiaries may be AI companies, chip suppliers, existing dominant firms, new startups, consumers, or some combination of all of them.
He raises several open questions: Will AI services become commodities? Will companies maintain differentiated products? Will suppliers like Nvidia preserve their margins? Will AI lower the barrier to starting new businesses?
We close by discussing which lessons investors still resist. Hank says some people remain skeptical of the finding that most stocks lose money over long horizons. But in his view, skewness and asymmetry are not quirks of one dataset; they are fundamental features of compounding random returns.
He compares public stock markets to venture capital: in both cases, many investments fail, but a small number of extreme winners make the asset class worthwhile.
Resources on Hank’s Research:
- One Hundred Years in the U.S. Stock Markets
- Do Stocks Outperform Treasury Bills?
- Measuring Investor OutcomesWhich U.S. Stocks Generated the Highest Long-Term Returns?
The Long Term Investor audio is edited by the team at The Podcast Consultant
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