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

I develop simple and intuitive bounds for the false discovery rate (FDR) in cross-sectional return predictability publications. The bounds can be calculated by plugging in summary statistics from previous papers and reliably bound the FDR in simulations that closely mimic cross-predictor correlations. Most bounds find that at least 75% of findings are true. The tightest bound finds at least 91% of findings are true. Surprisingly, the estimates in Harvey, Liu, and Zhu (2016) imply a similar FDR. I explain how Harvey et al.'s conclusion that most findings are false stems from equating "false" and "insignificant."