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Improving factor momentum: Statistical significance matters

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  • Liu, Yangyi
  • Luo, Ronghua
  • Zhao, Senyang

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.

Suggested Citation

  • Liu, Yangyi & Luo, Ronghua & Zhao, Senyang, 2023. "Improving factor momentum: Statistical significance matters," Economics Letters, Elsevier, vol. 233(C).
    • Handle: RePEc:eee:ecolet:v:233:y:2023:i:c:s0165176523004706
    DOI: 10.1016/j.econlet.2023.111444
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    References listed on IDEAS

    as
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    7. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Statistical factor momentum; Pairwise t-test; Volatility; False discoveries;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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