Sudden market downturns can be unsettling. But historically, US equity returns following sharp declines have, on average, been positive. A broad market index tracking data since 1926 in the US shows that stocks have tended to deliver positive returns over one-year, three-year, and five-year periods following steep declines. Cumulative returns show this trend to striking effect, as seen in Exhibit 1.

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Investing and health can be two of the most important things in life, but sometimes they also can be the most confusing. There’s so much data and advice, so many articles—and unfortunately, they often don’t agree.

So, I wasn’t surprised to see that one of the bestselling books of the year is physician Peter Attia’s Outlive: The Science & Art of Longevity, which looks at recent scientific research on aging to explore strategies for not only living longer but also living healthier. I was struck by the parallels between how he talks about health and how we at Dimensional think about investing.

Here are some of his main observations about health:

• There's no one-size-fits-all solution.
• There are no quick fixes.
• It's better to prevent problems than find yourself in the position of having to fix them.

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How many investors appreciate the role financial science has had in the way we invest today? Before financial science emerged in the middle of the 20th century, there was only one way of investing—the traditional active way. People would research individual stocks and bonds and buy a few that they thought were underpriced.

That changed when Harry Markowitz, a Ph.D. candidate at the University of Chicago, introduced the idea of Modern Portfolio Theory in his 1952 paper “Portfolio Selection.” This theoretical paper suggests the steps an investor can take to build a portfolio that balances efficiently the tradeoff between expected return and volatility. It was the first big breakthrough in financial science and is still taught today as one of the cornerstones of financial theory. Markowitz later became a Nobel laureate for this work.

Traditional active stock picking, however, remained about the only investment approach until the early 1970s, when indexing emerged. This new approach offered broad diversification, low cost, and transparency by holding all securities in a market index. These characteristics appealed to investors who were familiar with the research of Michael Jensen, another Ph.D. student at Chicago, who in 1968 illustrated how few fund managers outperformed the market, especially after their fees and costs were deducted.

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Can artificial intelligence help pick stocks? More specifically, can investors use AI to determine the fair price of a stock or a bond? I bet a lot of people right now would say yes, given recent advances that allow for the processing of ever greater amounts of information.

I think my AI is better than all the other ones out there. My AI is the market.

For example, pick a stock. Check the price. Why is it that exact price? Because an equal number of buyers and sellers think they are getting a good deal when they sell or buy it at exactly that time. They make those judgments using every piece of information available to them, both public and private. The market is the world’s largest information processing machine, which creates a price for every publicly traded stock and bond.

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If you were to poll strangers on what comes to mind when they hear the term AI (artificial intelligence), I suspect the two most likely answers would be Skynet or ChatGPT. The generative chat program launched in 2022 seems to have drawn the most mainstream attention to AI applications since Arnold Schwarzenegger promised he’d be back. But the history of AI tools is far older than ChatGPT, although less dramatic than its depiction in 1990s science fiction films. And from an investment standpoint, artificial intelligence pales in comparison to the informational content of the market’s AI—aggregate intelligence.

ChatGPT is but a recent example of AI. One watershed moment came in 1997 when the machine named Deep Blue became the first computer to secure victory in a match against a chess grandmaster. In the mid-2000s, IBM researchers created the Watson computer to compete with star Jeopardy! contestants, ultimately defeating two of the show’s most decorated past champions. And how many of us routinely dispense orders to, and receive suggestions from, Siri or Alexa?

The common thread among these examples is that each represents a tool that processes and organizes data to identify patterns and summarize information or make suggestions. This type of interaction with AI has grown to permeate our everyday lives. Have you noticed your phone offer an unsolicited ETA for your commute when you get in your car? Does your text app suggest grammar revisions based on the context of your overall message? Congrats—you’re an AI user, even if you’ve never opened a ChatGPT session.

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