Money Minded: How to Psychoanalyze the Stock Market

In the end, Peterson's long-short strategy simulation came up with a 34 percent return between July 2006 and November 2007. (In that period, the S&P 500 earned a 13 percent return.) But four stocks aren't enough to make a successful portfolio: If one of them crashes for impossible-to-anticipate reasons, money will be lost no matter what the other three stocks are doing. And a year isn't long enough to assure investors that his approach works. Eventually, as the program becomes more sophisticated and has a larger historical database to refer to, the list of stocks and potential plays will grow. But the proof, as Peterson repeatedly emphasizes, will be in the results. "The initial investors are happy with the current strategy," he writes in an e-mail. "The later investors will be convinced by performance."

When it comes to the real magic that makes MarketPsy Capital different—the way its software times the trade—Peterson won't get into details. It's fairly simple to detect irrationality in the market as a whole, and even to detect it in a particular stock, as Thaler pointed out in 2001, but before now it hasn't been possible to reliably predict when that irrationality will stop. No one ever really knows when it's time to cash in their chips.

Peterson only hints at how his software solves the problem: "There are systematic ways that people mis-expect, and they can be detected in language." He admits to certain technical barriers—it's been difficult to find sources for the quantity of information his software requires, and it's still building a reliable historical database for each term it encounters—but he insists the bugs are coming out and that the software will soon be capable of even more complicated predictions. "More advanced artificial-intelligence strategies look good in statistical testing, but they aren't ready to be implemented into trading until this summer."

The fund has enough seed money from investors to put his approach to the test this spring. If it produces a good track record—which is harder in the real world, where real people are coping with anxieties about losing real money, than in simulations—more investors will surely come. But will they stay when the next dot-com-style hysteria whirls through the market?

And if neuroinvesting takes hold on a large scale, and investors resist the ingrained urge to follow each other around like sheep, going against the crowd may no longer be as profitable as it seems to be today. But don't bet on it. Herding, as is true of most of our irrational habits, has been with us since we walked upright. It's unlikely we'll wise up anytime soon.

Robert Armstrong is a financial analyst in New York City. Jacob Ward is deputy editor of Popular Science.

Hee-yah, Investors! Get Along There!

Robert Prechter, president of Elliott Wave International, a financial-market forecasting firm, wrote in 2001 that the herding impulse that evolved in humans is a crippling investment handicap. Yet, he points out, it’s universal. Individual investors [see chart above] “commit more money to the market as it rises and less as it falls, behavior opposite from that which would generate profits.” And professionals can’t help, because (as shown by the cash held at their institutions as related to the level of the S&P 500 Composite index) they herd in the market’s direction along with the public.

How the Software Reads Warning Signs

The software developed by Richard Peterson and his partners scans media including blogs, earnings reports and analyst research every minute to arrive at hourly forecasts about where certain stocks are headed. Using a library of keywords—”exuberance,” “bankruptcy,” “crisis of confidence”—and calculations based on the historical relationship between those words and sources and movement on the market, it develops a “risk rating” for the stock in question.

The rating ranges from –5 (stocks likely to go up) to +5 (likely to go down), with the timing of the predicted change depending on how long they’ve been in that risk category and what words are being used to describe them. Seventy percent of the stocks Peterson covers fall between –2 and +2. It’s the stocks that fall outside this middle range that offer the greatest opportunity to make money as they move.