Understanding Momentum and Reversals
32 Pages Posted: 19 Jun 2020 Last revised: 24 Feb 2021
Date Written: May 1, 2018
Stock momentum, long-term reversal, and other past return characteristics that predict future returns also predict future realized betas, suggesting these characteristics capture time-varying risk compensation. We formalize this argument with a conditional factor pricing model. Using instrumented principal components analysis, we estimate latent factors with time-varying factor loadings that depend on observable firm characteristics. We show that factor loadings vary significantly over time, even at short horizons over which the momentum phenomenon operates (one year), and this variation captures reliable conditional risk premia missed by other factor models commonly used in the literature. Our estimates of conditional risk exposure can explain a sizable fraction of momentum and long-term reversal returns and can be used to generate even stronger return predictions.
Keywords: momentum, reversal, factor model, conditional betas, conditional ex- pected returns, IPCA
JEL Classification: G10,G11,G12,G14
Suggested Citation: Suggested Citation