We study the implications of learning in an environment where the true model of the world is a multivariate one, but where agents update only over the class of simple univariate models. If a particular simple model does a poor job of forecasting over a period of time, it is eventually discarded in favor of an alternative yet equally simple model that would have done better over the same period. This theory makes several distinctive predictions, which, for concreteness, we develop in a stock-market setting. For example, starting with symmetric and homoskedastic fundamentals, the theory yields forecastable variation in the size of the value/glamour differential, in volatility, and in the skewness of returns. Some of these features mirror familiar accounts of stock-price bubbles.
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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number
10013.
Length: Date of creation: Oct 2003 Date of revision: Handle: RePEc:nbr:nberwo:10013
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Find related papers by JEL classification: D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information G12 - Financial Economics - - General Financial Markets - - - Asset Pricing
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References listed on IDEAS 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.:
Nicholas Barberis & Andrei Shleifer & Robert W. Vishny, 1997.
"A Model of Investor Sentiment,"
NBER Working Papers
5926, National Bureau of Economic Research, Inc.
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