Most claimed statistical findings in cross-sectional return predictability are likely true

TL;DR

This paper develops bounds on the false discovery rate in cross-sectional return predictability studies, showing that most claimed findings are likely true, with FDR estimates generally below 25%.

Contribution

It introduces simple bounds on the FDR using summary statistics, reconciling previous disparate estimates and suggesting most findings are likely valid.

Findings

FDR is at most 25% in most studies

Refined bounds suggest FDR is below 9%

Random ratios often produce significant results by chance

Abstract

The false discovery rate (FDR) measures the share of false positives in a set of statistical tests. I develop simple and intuitive bounds on the FDR in cross-sectional predictability publications. The simplest bound requires just a few lines of math and finds $\text{FDR} \le 25\%$ based on summary statistics in eight out of nine previous studies. A more refined bound finds $\text{FDR} \le 9\%$. The FDR is small because randomly selecting accounting ratios produces statistically significant predictability far more often than would occur if there were no predictability. The bounds also reconcile the disparate FDR estimates in the literature.