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Asset Return Dynamics and Learning

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Author Info

  • Wiliam Branch

    (University of Californis - Irvine)

  • George W. Evans

    ()
    (University of Oregon Economics Department)

Abstract

This paper advocates a theory of expectation formation that incorporates many of the central motivations of behavioral finance theory while retaining much of the discipline of the rational expectations approach. We provide a framework in which agents, in an asset pricing model, underparameterize their forecasting model in a spirit similar to Hong, Stein, and Yu (2005) and Barberis, Shleifer, and Vishny (1998), except that the parameters of the forecasting model, and the choice of predictor, are determined jointly in equilibrium. We show that multiple equilibria can exist even if agents choose only models that maximize (risk-adjusted) expected profits. A real-time learning formulation yields endogenous switching between equilibria. We demonstrate that a real-time learning version of the model, calibrated to U.S. stock data, is capable of reproducing many of the salient empirical regularities in excess return dynamics such as under/overreaction, persistence, and volatility clustering.

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Bibliographic Info

Paper provided by University of Oregon Economics Department in its series University of Oregon Economics Department Working Papers with number 2006-14.

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Length: 40
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Handle: RePEc:ore:uoecwp:2006-14

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Related research

Keywords: Asset pricing; misspecification; behavioral finance; predictability; adaptive learning;

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