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New Evidence on Conditional Factor Models

Published online by Cambridge University Press:  20 December 2018

Ilan Cooper and
Paulo Maio
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Abstract

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We estimate conditional multifactor models over a large cross section of stock returns matching 25 CAPM anomalies. Using conditioning information associated with different instruments improves the performance of the Hou, Xue, and Zhang (HXZ) (2015) and Fama and French (FF) (2015), (2016) models. The largest increase in performance holds for momentum, investment, and intangibles-based anomalies. Yet, there are significant differences in the performance of scaled models: HXZ clearly dominates FF in explaining momentum and profitability anomalies, while the converse holds for value–growth anomalies. Thus, the asset pricing implications of alternative investment and profitability factors (in a conditional setting) differ in a nontrivial way.

Information

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 54 , Issue 5 , October 2019, pp. 1975 - 2016
Copyright
Copyright © Michael G. Foster School of Business, University of Washington 2018 

Footnotes

1

We thank Frederico Belo, Stefanos Delikouras, Marie Lambert, David Rapach, Gideon Saar, Thiago de Oliveira Souza, Ariel Viale, and seminar participants at the 2016 MFA meeting, Tel Aviv University, the University of Southern Denmark, the 2016 FMA Europe meeting, and the 2016 FMA for helpful comments. Jennifer Conrad (the editor) and Chen Xue (the referee) deserve special thanks. We are grateful to Kenneth French, Amit Goyal, Robert Shiller, and Lu Zhang for providing stock market data. All remaining errors are our own.

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