We provide data and code that successfully reproduces nearly all crosssectional stock return predictors. Unlike most metastudies, we carefully examine the original papers to determine whether our predictability tests should produce t-stats above 1.96. For the 180 predictors that were clearly...

We provide data and code that successfully reproduces nearly all crosssectional stock return predictors. Our 319 characteristics draw from previous meta-studies, but we differ by comparing our t-stats to the original papers' results. For the 161 characteristics that were clearly significant in...

We provide data and code that successfully reproduces nearly all cross-sectional stock return predictors. Unlike most metastudies, we carefully examine the original papers to determine whether our predictability tests should produce t-stats above 1.96. For the 180 predictors that were clearly...

To examine whether theory helps predict the cross-section of returns, we combine text analysis of publications with out-of-sample tests. Based on the original texts, only 18% of predictors are attributed to risk-based theory. 59% are attributed to mispricing, and 23% have uncertain origins....

Harvey, Liu, and Zhu (2016) “argue that most claimed research findings in financial economics are likely false.” Surprisingly, their false discovery rate (FDR) estimates suggest most are true. I revisit their results by developing non- and semi-parametric FDR estimators that account for...

Mining 29,000 accounting ratios for t-statistics over 2.0 leads to cross-sectional predictability similar to the peer review process. For both methods, about 50% of predictability remains after the original sample periods. Data mining generates other features of peer review including the rise in...