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We complement the conditional capital asset pricing model (CAPM) by introducing unobservable long-run changes in risk factor loadings. In this environment, investors rationally “learn” the long-run level of factor loading by observing 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 ratio, our learning-augmented conditional CAPM fails to be rejected.
For a published version of this report, see Tobias Adrian and Francesco Franzoni, "Learning about Beta: Time-Varying Factor Loadings, Expected Returns, and the Conditional CAPM," Journal of Empirical Finance 16, no. 4: 537-556.