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Open Source Cross-Sectional Asset Pricing

Andrew Y. Chen and Tom Zimmermann

Critical Finance Review, 2022, vol. 11, issue 2, 207-264

Abstract: We provide data and code that successfully reproduces nearly all cross-sectional 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 long-short t-stats finds a slope of 0.88 and an R2 of 82%. Mean returns are monotonic in predictive signals at the characteristic level. The remaining 114 characteristics were insignificant in the original papers or are modifications of the originals created by Hou et al. (2020). These remaining characteristics are almost always significant if the original characteristic was also significant.

Keywords: Stock market anomalies; Replication; Asset pricing (search for similar items in EconPapers)
JEL-codes: G10 G12 (search for similar items in EconPapers)
Date: 2022
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http://dx.doi.org/10.1561/104.00000112 (application/xml)

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Working Paper: Open Source Cross-Sectional Asset Pricing (2021) Downloads
Working Paper: Open source cross-sectional asset pricing (2020) Downloads
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