Asset Return Dynamics and Learning

dc.contributor.authorBranch, William A.
dc.contributor.authorEvans, George W., 1949-
dc.date.accessioned2007-01-16T17:29:00Z
dc.date.available2007-01-16T17:29:00Z
dc.date.issued2006-11-13
dc.description40 p.en
dc.description.abstractThis 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 realtime 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.en
dc.format.extent364168 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/1794/3797
dc.language.isoen_USen
dc.publisherUniversity of Oregon, Dept of Economicsen
dc.relation.ispartofseriesUniversity of Oregon Economics Department Working Papers ; 2006-14en
dc.subjectAsset pricingen
dc.subjectMisspecificationen
dc.subjectBehavioral financeen
dc.subjectPredictabilityen
dc.subjectAdaptive learningen
dc.titleAsset Return Dynamics and Learningen
dc.typeWorking Paperen

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