Open Source Cross-Sectional Asset Pricing

66 Pages Posted: 30 Jun 2021 Last revised: 7 Sep 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: June 1, 2021

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 0.90 and an R2 of 83%. 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.

JEL Classification: G10

Suggested Citation

Chen, Andrew Y. and Zimmermann, Tom, Open Source Cross-Sectional Asset Pricing (June 1, 2021). FEDS Working Paper No. 2021-37, Available at SSRN: https://ssrn.com/abstract=3875093 or http://dx.doi.org/10.17016/FEDS.2021.037

Andrew Y. Chen (Contact Author)

Board of Governors of the Federal Reserve System ( email )

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HOME PAGE: http://sites.google.com/site/chenandrewy/

Tom Zimmermann

University of Cologne ( email )

Albertus-Magnus-Platz
Cologne, 50923
Germany

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