Open Source Cross-Sectional Asset Pricing
Andrew Chen () and
No 2021-037, Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.)
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 the original papers, 98% of our long-short portfolios find t-stats above 1.96. For the 44 characteristics that had mixed evidence, our reproductions find t-stats of 2 on average. A regression of reproduced t-stats on original longshort t-stats finds a slope of 0.90 and an R2 of 83%. Mean returns aremonotonic in predictive signals at the characteristic level. The remaining 114 characteristics were insignificant in the original papers or are modifications of the originals created byHou, Xue, and Zhang (2020). These remaining characteristics are almost always significant if the original characteristic was also significant.
Keywords: replications; Stock market anomalies (search for similar items in EconPapers)
JEL-codes: G10 (search for similar items in EconPapers)
Pages: 66 p.
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Journal Article: Open Source Cross-Sectional Asset Pricing (2022)
Working Paper: Open source cross-sectional asset pricing (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedgfe:2021-37
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