ETF Trends
ETF Trends

On Friday, May 1, I attended The Alpha and Beta of Factor Investing conference in New York, hosted by The Wharton School’s Jacobs Levy Equity Management Center. The center is focused on quantitative financial research, and the organizers put together a fantastic, education-packed agenda.

Professor Jeremy Siegel and I sat down with Campbell Harvey, professor at Duke University, to speak about his presentation, which challenges the efficacy of many factor-based investment strategies.

Too Many False Positives?

Harvey believes both the academic finance profession and industry practitioners have too loose a standard for determining the success of new investment factors—causing a proliferation of new investment products that he deems destined to fail.

He discusses how the finance community and statisticians in general are accustomed to using a two-sigma rule to test the significance of various effects on investment strategies—which is to say, if the factor in question is more than two standard deviations from zero, statisticians would say there is 95% confidence that this factor could outperform the market.

Harvey went through 316 published factors—harvested from the premiere financial journals—that have been shown to outperform a passive portfolio of the market. He believes many of these findings are false, illustrating a type I error, or a false positive result. This suggests that while the data looked good in the past, in reality much of it was a fluke.

Harvey discussed research showing that stocks starting with a certain letter of the alphabet outperformed the market. But there are 26 letters, so there is a 4% chance that any one of these would happen to outperform. This letter-based investment strategy is not a sound basis for a forward-looking investment strategy, despite its success in the historical data.

Evolution Hardwired for Type I Errors

Harvey pointed out how evolution has caused humans to favor these type I errors. His presentation went back in history to the gazelles on the Serengeti to illustrate.

Let’s say a gazelle heard a rustling in the grass and took off, expending energy to move from danger. But it turned out there was nothing dangerous lurking in the grass; it was just wind. This gazelle made a classic type I error—it had a false positive reading of danger.

A type II error would be if the gazelle heard the rustling in the grass, did not move and then was eaten by a cheetah.

Harvey believes evolution thus favored those who were hardwired and had a predisposition to make more type I errors—gazelles or early humans. Those who made more type I errors survived more often.

The implication is that we all like looking for patterns in data, even when none truly exists and much of it is just noise and coincidence.

Which Factors Are “Campbell Harvey Strong”?

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