Open Source Cross-Sectional Asset Pricing

45 Pages Posted: 12 Jun 2020

See all articles by Andrew Y. Chen

Andrew Y. Chen

Federal Reserve Board

Tom Zimmermann

University of Cologne

Date Written: May 18, 2020


We provide data and code that successfully reproduces nearly all cross-sectional stock return predictors. Unlike most metastudies, we carefully examine the original papers to determine whether our predictability tests should produce t-stats above 1.96. For the 180 predictors that were clearly significant in the original papers, 98% of our reproductions find t-stats above 1.96. For the 30 predictors that had mixed evidence, our reproductions find t-stats of 2 on average. We include an additional 105 characteristics and 945 portfolios with alternative rebalancing frequencies to nest variables used in other metastudies. Our data covers all portfolios in Hou, Xue and Zhang (2017); 98% of the portfolios in McLean and Pontiff (2016); 90% of the characteristics from Green, Hand, and Zhang (2017); and 90% of the firm-level predictors in Harvey, Liu, and Zhu (2016) that use widely-available data.

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 18, 2020). Available at SSRN: or

Andrew Y. Chen (Contact Author)

Federal Reserve Board ( email )

20th and C Streets, NW
Washington, DC 20551
United States
202-973-6941 (Phone)


Tom Zimmermann

University of Cologne ( email )

Cologne, 50923

Here is the Coronavirus
related research on SSRN

Paper statistics

Abstract Views
PlumX Metrics