Time‐Series Factor Modeling and Selection

Abstract

The article proposes a statistical time‐series factor model that incorporates deterministic orthogonal trend polynomials. Such polynomials allow capturing variation in returns without initially identifying a set of robust time‐series factors. This modeling approach can serve as a coherent basis for testing and selecting the most relevant factors among a set of possible ones. Additionally, it can help identify whether any factors are missing from a time‐series asset pricing model. The use of the proposed model and empirical strategy is illustrated by two empirical applications from the literature, yielding results related to the Fama‐French five‐factor model and the factor zoo.