Adrian, Tobias Franzoni, Francesco 2009 We amend the conditional CAPM to allow for unobservable long-run changes in risk factor loadings. In this environment, investors rationally “learn” the long-run level of factor loadings from the observation of realized returns. As a consequence of this assumption, we model conditional betas 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 passes the specification tests. application/pdf https://susi.usi.ch/global/documents/318327 https://localhost:5000/ark:/12658/srd1318327 https://susi.usi.ch/documents/318327/files/Franzoni_JFE_2009_2.pdf eng info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jempfin.2009.02.003 info:eu-repo/semantics/altIdentifier/ark/12658/srd1318327 info:eu-repo/semantics/openAccess License undefined Journal of empirical finance. - Elsevier. - 2009, vol. 16, no. 4, p. 537-556 Beta CAPM kalman filter anomalies value premium info:eu-repo/classification/udc/33 Learning about beta : time-varying factor loadings, expected returns, and the conditional CAPM http://purl.org/coar/resource_type/c_6501