Learning about Beta: Time-Varying Factor Loadings, Expected Returns,and the Conditional CAPM

We complement the conditional CAPM by introducing unobservable long-run changes in risk factor loadings. In this environment, investors rationally `learn' the long-level of factor loadings from the observation of realized returns. As a direct consequence of this assumption, conditional betas are modeled using the Kalman ¯lter. Because of its focus on low frequency variation in betas, our approach circumvents recent criticisms of the conditional CAPM. When tested on portfolios sorted by size and book-to-market, our learning-augmented conditional CAPM fails to be rejected.