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Open source cross-sectional asset pricing

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  • Chen, Andrew Y.
  • Zimmermann, Tom

Abstract

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.

Suggested Citation

  • Chen, Andrew Y. & Zimmermann, Tom, 2020. "Open source cross-sectional asset pricing," CFR Working Papers 20-04, University of Cologne, Centre for Financial Research (CFR).
  • Handle: RePEc:zbw:cfrwps:2004
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    1. Harvey, Campbell R. & Liu, Yan, 2021. "Lucky factors," Journal of Financial Economics, Elsevier, vol. 141(2), pages 413-435.
    2. Thomas Conlon & John Cotter & Iason Kynigakis, 2021. "Machine Learning and Factor-Based Portfolio Optimization," Papers 2107.13866, arXiv.org.
    3. Antoine Falck & Adam Rej & David Thesmar, 2021. "Why and how systematic strategies decay," Papers 2105.01380, arXiv.org.
    4. Thomas Conlon & John Cotter & Iason Kynigakis, 2021. "Machine Learning and Factor-Based Portfolio Optimization," Working Papers 202111, Geary Institute, University College Dublin.

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    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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