Arbeitspapier
Open source cross-sectional asset pricing
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: CFR Working Paper ; No. 20-04
- Klassifikation
-
Wirtschaft
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Chen, Andrew Y.
Zimmermann, Tom
- Ereignis
-
Veröffentlichung
- (wer)
-
University of Cologne, Centre for Financial Research (CFR)
- (wo)
-
Cologne
- (wann)
-
2020
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Chen, Andrew Y.
- Zimmermann, Tom
- University of Cologne, Centre for Financial Research (CFR)
Entstanden
- 2020
Ähnliche Objekte (12)
Empirical essays on cross-sectional asset pricing and institutional investors
GMM weighting matrices incross-sectional asset pricing tests
Cross-sectional tests of the capital asset pricing model in stock markets of the U.K. and the U.S.
Pricing model performance and the two-pass cross-sectional regression methodology
Collaborative capacity of open source hardware communities: A cross-sectional study
Outliers in Cross-Sectional Regression
Option characteristics as cross-sectional predictors
CSAR: The Cross-Sectional Autoregression Model
Cross-sectional and aggregate labor supply
Option characteristics as cross-sectional predictors
Joint extreme events in equity returns and liquidity and their cross-sectional pricing implications
Asset pricing
Empirical essays on cross-sectional asset pricing and institutional investors
GMM weighting matrices incross-sectional asset pricing tests
Cross-sectional tests of the capital asset pricing model in stock markets of the U.K. and the U.S.
Pricing model performance and the two-pass cross-sectional regression methodology
Collaborative capacity of open source hardware communities: A cross-sectional study
Outliers in Cross-Sectional Regression
Option characteristics as cross-sectional predictors
CSAR: The Cross-Sectional Autoregression Model
Cross-sectional and aggregate labor supply
Option characteristics as cross-sectional predictors
Joint extreme events in equity returns and liquidity and their cross-sectional pricing implications
Asset pricing
Empirical essays on cross-sectional asset pricing and institutional investors
GMM weighting matrices incross-sectional asset pricing tests
Cross-sectional tests of the capital asset pricing model in stock markets of the U.K. and the U.S.
Pricing model performance and the two-pass cross-sectional regression methodology
Collaborative capacity of open source hardware communities: A cross-sectional study
Outliers in Cross-Sectional Regression
Option characteristics as cross-sectional predictors
CSAR: The Cross-Sectional Autoregression Model
Cross-sectional and aggregate labor supply
Option characteristics as cross-sectional predictors
Joint extreme events in equity returns and liquidity and their cross-sectional pricing implications