Asset Return Dynamics and Learning
AbstractThis article 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 (2007) 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 regime-switching returns and volatilities, as recently identified by Guidolin and Timmermann (2007). The Author 2010. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: firstname.lastname@example.org., Oxford University Press.
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Bibliographic InfoArticle provided by Society for Financial Studies in its journal The Review of Financial Studies.
Volume (Year): 23 (2010)
Issue (Month): 4 (April)
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Other versions of this item:
- Wiliam Branch & George W. Evans, . "Asset Return Dynamics and Learning," University of Oregon Economics Department Working Papers 2006-14, University of Oregon Economics Department.
- G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
- G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
- D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
- D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information
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