We examine the dynamic properties of equilibrium stock returns in an incomplete information economy in which the agents need to learn the hidden state of the endowment process. We consider both the case of optimal Bayesian learning and suboptimal learning, including near-rational learning, over- or under-confidence, optimism or pessimism, adaptive learning, and limited memory. We find that Bayesian learning can quantitatively explain short-run momentum, long-run mean-reversion, predictability, volatility clustering, and leverage effects in stock returns. Only over-confidence can marginally improve some aspects of the model (add short-run momentum) without substantially deteriorating other aspects. We conclude that the success of the incomplete information model is quite dependent on optimally learning agents.
Download Info
To our knowledge, this item is not available for
download. To find whether it is available, there are three
options:
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page
whether it is in fact available.
3. Perform a search for a similarly titled item that would be
available.
Cited by: (explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)
Larry Epstein & Martin Schneider, 2002.
"Learning Under Ambiguity,"
RCER Working Papers
497, University of Rochester - Center for Economic Research (RCER), revised Mar 2005.
[Downloadable!]
Other versions: