Open source cross-sectional asset pricing - Andrew Y. Chen; Tom Zimmermann (University of Cologne, Centre for Financial Research (CFR), 2020)
We provide data and code that successfully reproduces nearly all crosssectional 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 widelyavailable data.
Keywords: 330