Critical Finance Review > Vol 11 > Issue 2

Open Source Cross-Sectional Asset Pricing

Andrew Y. Chen, Federal Reserve Board, USA, , Tom Zimmermann, University of Cologne, Germany,
Suggested Citation
Andrew Y. Chen and Tom Zimmermann (2022), "Open Source Cross-Sectional Asset Pricing", Critical Finance Review: Vol. 11: No. 2, pp 207-264.

Publication Date: 03 May 2022
© 2022 Andrew Y. Chen and Tom Zimmermann
Stock market anomaliesReplicationAsset pricing


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In this article:
1. Introduction 
2. Methods: Characteristic Selection, Reproduction Methods, and Predictability Categories 
3. Literature-Level Reproduction Performance 
4. Characteristic-Level Reproduction Performance 
5. Additional Evidence of Dataset Quality 
6. Conclusion 


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.