Open Source Cross-Sectional Asset Pricing

被引:51
|
作者
Chen, Andrew Y. [1 ]
Zimmermann, Tom [2 ]
机构
[1] Fed Reserve Board, Washington, DC USA
[2] Univ Cologne, Cologne, Germany
来源
CRITICAL FINANCE REVIEW | 2022年 / 11卷 / 02期
关键词
Stock market anomalies; Replication; Asset pricing; LONG-RUN PERFORMANCE; STOCK RETURNS; FINANCIAL CONSTRAINTS; FUNDAMENTAL ANALYSIS; ANALYSTS FORECASTS; FUTURE EARNINGS; LIQUIDITY RISK; EQUITY; INFORMATION; INVESTMENT;
D O I
10.1561/104.00000112
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
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.
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页码:207 / 264
页数:58
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