90 Pages Posted: 22 Nov 2014 Last revised: 17 Oct 2019
Date Written: October 15, 2019
We propose a new method to select amongst a large group of candidate factors -- many of which might arise as a result of data mining -- that purport to explain the cross-section of expected returns. The method is robust to general distributional characteristics of both factor and asset returns. We allow for the possibility of time-series as well as cross-sectional dependence. The technique accommodates a wide range of test statistics. Our method can be applied to both asset pricing tests based on portfolio sorts as well as tests using individual asset returns. In contrast to recent asset pricing research, our study of individual stocks finds that the original market factor is by far the most important factor in explaining the cross-section of expected returns.
Note: This paper was formerly circulated under the title "How Many Factors?"
Keywords: Factors, Variable selection, Bootstrap, Data mining, Orthogonalization, Multiple testing, Predictive regressions, Fama-MacBeth, GRS, Performance evaluation, Return prediction
JEL Classification: G12, G14, C12, C21, C22, C31, C32
Suggested Citation: Suggested Citation