  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 that 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 misinterpreting
``insignificant factor'' as ``false discovery.''
