Improving factor momentum: Statistical significance matters

Yangyi Liu, Ronghua Luo and Senyang Zhao

Economics Letters, 2023, vol. 233, issue C

Abstract: Factor selection in the crowded “factor zoo” presents a significant challenge. This study introduces the statistical factor momentum (SFMOM), a novel approach employing pairwise t-test procedures to adeptly balance Type I and Type II errors, thereby enhancing factor momentum. Through empirical analysis of 207 factors, we demonstrate SFMOM’s superior performance, particularly in long-short portfolios. SFMOM prefers low-volatility factors and its effectiveness is most pronounced during periods of substantial dispersion in factors’ risk-adjusted performance. Our study offers a new perspective on factor selection and a practical tool for portfolio managers, and the methodology can be applied to other markets.

Keywords: Statistical factor momentum; Pairwise t-test; Volatility; False discoveries (search for similar items in EconPapers)
JEL-codes: G11 G12 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:233:y:2023:i:c:s0165176523004706

DOI: 10.1016/j.econlet.2023.111444

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