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:|
|Contact details of provider:|| Postal: 1285 University of Oregon, 435 PLC, Eugene, OR 97403-1285|
Phone: (541) 346-8845
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 you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.