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.
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3 May 2022
Research Article|
May 03 2022
Open Source Cross-Sectional Asset Pricing
Tom Zimmermann
University of Cologne
, Germany
* First posted to SSRN: May 18, 2020. We thank Ivo Welch for encouraging us to work on this project, and thank Shane Corwin, Paul Schultz, Luis Palacios, Rabih Moussawi, and Denys Glushkov for making their code publicly available. We also thank an anonymous referee, Guillaume Coqueret, Sebastian Hillenbrand, Ryan Israelsen (discussant), Liang Jiang, Yang Liu (from Tsinghua Finance), Jeffrey Pontiff (discussant), Yue Zhao, and anonymous contributors to our GitHub repo for helpful comments. We are grateful to Sebastian Weibels, Arne Rodloff, and Antonio Gil de Rubio Cruz for excellent research assistance. The project received support from the Deutsche Forschungsgemeinschaft (DFG) under Germany’s Excellence Strategy – EXC2126/1-39083886. The views expressed herein are those of the authors and do not necessarily reflect the position of the Board of Governors of the Federal Reserve or the Federal Reserve System.
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* First posted to SSRN: May 18, 2020. We thank Ivo Welch for encouraging us to work on this project, and thank Shane Corwin, Paul Schultz, Luis Palacios, Rabih Moussawi, and Denys Glushkov for making their code publicly available. We also thank an anonymous referee, Guillaume Coqueret, Sebastian Hillenbrand, Ryan Israelsen (discussant), Liang Jiang, Yang Liu (from Tsinghua Finance), Jeffrey Pontiff (discussant), Yue Zhao, and anonymous contributors to our GitHub repo for helpful comments. We are grateful to Sebastian Weibels, Arne Rodloff, and Antonio Gil de Rubio Cruz for excellent research assistance. The project received support from the Deutsche Forschungsgemeinschaft (DFG) under Germany’s Excellence Strategy – EXC2126/1-39083886. The views expressed herein are those of the authors and do not necessarily reflect the position of the Board of Governors of the Federal Reserve or the Federal Reserve System.
Online ISSN: 2164-5760
Print ISSN: 2164-5744
© 2022 Andrew Y. Chen and Tom Zimmermann
2022
Andrew Y. Chen and Tom Zimmermann
Licensed re-use rights only
Critical Finance Review (2022) 11 (2): 207–264.
Citation
Chen AY, Zimmermann T (2022), "Open Source Cross-Sectional Asset Pricing". Critical Finance Review, Vol. 11 No. 2 pp. 207–264, doi: https://doi.org/10.1561/104.00000112
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