Estimating Time-Varying Factor Exposures

Abstract

We develop a methodology to estimate dynamic factor loadings using cross-sectional risk characteristics, which is especially useful when factor loadings significantly vary over time. In comparison, standard regression approaches assume the factor loadings are constant over a particular window. Applying the methodology to a dataset of U.S.-domiciled mutual funds we distinguish the components of active returns attributable to (1) constant factor exposures, for example, a tilt to value stocks; (2) time-varying factor exposures; and (3) security selection. The decomposition of active returns into these three components yields valuable insight into how managers generate excess returns. We show that there is diversity in factor concentration across managers and styles. For example, large-cap growth funds show the greatest concentration in two factors, momentum and quality, whereas large-cap blend funds have the most factor diversity. Finally, common measures to gauge manager skill may be misleading. For example, we find no evidence that active share is associated with larger active returns; rather the opposite is true across the whole sample when controlling for factors such as fund size and fees.