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Open Source Cross-Sectional Asset Pricing

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Abstract

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.

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  • Andrew Y. Chen & Tom Zimmermann, 2021. "Open Source Cross-Sectional Asset Pricing," Finance and Economics Discussion Series 2021-037, Board of Governors of the Federal Reserve System (U.S.).
  • Handle: RePEc:fip:fedgfe:2021-37
    DOI: 10.17016/FEDS.2021.037
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    3. Antoine Falck & Adam Rej & David Thesmar, 2021. "Why and how systematic strategies decay," Papers 2105.01380, arXiv.org.

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    More about this item

    Keywords

    Replication; Stock market anomalies;

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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