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Ivory Tower Wonks Help Traders Make a Quick Buck – Bloomberg

May 12
14:10 2017

We live in an empirical age. P-values, R-squareds, and other statistical termshave become a universal language for describing the world. So perhaps it was inevitable that the contrarians and skeptics of our age would turn their wrathful eye on the excesses and missteps of statistical analysis. In recent years, there have been a number of high-profile academic papers demonstrating that science is chock full of bogus statistical results.

My personal favorite is John Ioannidis’ 2005 paper, “Why Most Published Research Findings are False.” Ioannidis shows that because researchers try many different statistical tests, a few are always likely to come out looking statistically significant, even if there’s really nothing there. This phenomenon goes by many names — p-hacking, data mining, data snooping and data dredging. Statistician Andrew Gelman points out that researchers don’t even need to try a bunch of tests for this to be a problem — all that’s required is that the researchers look at the data and decide in advance which tests have a better chance of yielding an eye-catching result. This bias can even be unconscious. In any case, the upshot is that any scientific literature that relies on statistical testing is likely to have a lot of false positives.

Finance is no exception. In 2014, Campbell Harvey, Yan Liu and Heqing Zhu wrote a paper called “…and the Cross-Section of Expected Returns.”  They argue that because of data mining, most of the factors that researchers claim predict investment returns will eventually turn out to be spurious.

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