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 filter. 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.