Open Source Cross-Sectional Asset Pricing

Critical Finance Review, Forthcoming

69 Pages Posted: 12 Jun 2020 Last revised: 17 Jun 2021

See all articles by Andrew Y. Chen

Andrew Y. Chen

Board of Governors of the Federal Reserve System

Tom Zimmermann

University of Cologne

Multiple version iconThere are 2 versions of this paper

Date Written: May 21, 2021


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 R^2 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, Xue, and Zhang (2020). These remaining characteristics are almost always significant if the original characteristic was also significant.

Keywords: stock market anomalies, replication, asset pricing

JEL Classification: G10, G12

Suggested Citation

Chen, Andrew Y. and Zimmermann, Tom, Open Source Cross-Sectional Asset Pricing (May 21, 2021). Critical Finance Review, Forthcoming, Available at SSRN: or

Andrew Y. Chen (Contact Author)

Board of Governors of the Federal Reserve System ( email )

20th Street and Constitution Avenue NW
Washington, DC 20551
United States
202-973-6941 (Phone)


Tom Zimmermann

University of Cologne ( email )

Cologne, 50923

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