Open source cross-sectional asset pricing

Andrew Y. Chen and Tom Zimmermann

No 20-04, CFR Working Papers from University of Cologne, Centre for Financial Research (CFR)

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.

Date: 2020
New Economics Papers: this item is included in nep-fmk
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
https://www.econstor.eu/bitstream/10419/219022/1/1700108905.pdf (application/pdf)

Related works:
Journal Article: Open Source Cross-Sectional Asset Pricing (2022)
Working Paper: Open Source Cross-Sectional Asset Pricing (2021)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:zbw:cfrwps:2004

Access Statistics for this paper

More papers in CFR Working Papers from University of Cologne, Centre for Financial Research (CFR) Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().