Universität zu Köln

Chen, Andrew Y. and Zimmermann, Tom (2022). Open Source Cross-Sectional Asset Pricing. Crit. Financ. Rev., 11 (2). S. 207 - 265. HANOVER: NOW PUBLISHERS INC. ISSN 2164-5760

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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 0.88 and an R-2 of 82%. 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 et al. (2020). These remaining characteristics are almost always significant if the original characteristic was also significant.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Chen, Andrew Y. UNSPECIFIED UNSPECIFIED UNSPECIFIED Zimmermann, Tom UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-670723
DOI:	10.1561/104.00000112
Journal or Publication Title:	Crit. Financ. Rev.
Volume:	11
Number:	2
Page Range:	S. 207 - 265
Date:	2022
Publisher:	NOW PUBLISHERS INC
Place of Publication:	HANOVER
ISSN:	2164-5760
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language LONG-RUN PERFORMANCE; STOCK RETURNS; FINANCIAL CONSTRAINTS; FUNDAMENTAL ANALYSIS; ANALYSTS FORECASTS; FUTURE EARNINGS; LIQUIDITY RISK; EQUITY; INFORMATION; INVESTMENT Multiple languages Business, Finance Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/67072