Learning about beta: Time-varying factor loadings, expected returns, and the conditional CAPM

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 th...

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Bibliographic Details
Published inJournal of empirical finance Vol. 16; no. 4; pp. 537 - 556
Main Authors Adrian, Tobias, Franzoni, Francesco
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.09.2009
Elsevier
SeriesJournal of Empirical Finance
Subjects
Anomalies
Asset pricing
Beta
Beta CAPM Kalman filter Anomalies Value premium
CAPM
Estimation
Investors
Kalman filter
Learning
Portfolio analysis
Stock returns
Time
Value premium
C11
Value premium
CAPM
Kalman filter
G12
Beta
Anomalies
Online AccessGet full text
ISSN0927-5398
1879-1727
DOI10.1016/j.jempfin.2009.02.003

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Summary: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.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:0927-5398
1879-1727
DOI:10.1016/j.jempfin.2009.02.003