Asset Return Dynamics and Learning
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.
|Date of creation:|
|Date of revision:|
|Contact details of provider:|| Postal: |
Phone: (541) 346-4661
Fax: (541) 346-1243
Web page: http://economics.uoregon.edu/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:ore:uoecwp:2006-14. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Bill Harbaugh)
If references are entirely missing, you can add them using this form.