Learning to Agree: A New Perspective on Price Drift

Andrea Giusto ()

Working Papers from Dalhousie University, Department of Economics

Abstract: This paper introduces statistical learning in an asset pricing model of differences of opinions. I show that the model converges globally to the unique rational-expectation equilibrium and furthermore I show that asset prices drift predictably in its neighborhood. Accordingly, the model offers a unifying perspective between two so-far mutually exclusive strands of the asset pricing literature. Learning preserves all the desirable features offered by the rational- expectations hypothesis (i.e. the traders use efficiently both the private and the public information available) while yet implying asset prices that drift predictably in the ex-ante sense of Banerjee, Kaniel, and Kremer (2009). Furthermore, I obtain a number of new empirically testable hypotheses related to price drift from a series of Monte Carlo explorations of a model of differences of opinion with learning.

Pages: 23 pages
Date: 2013-12-29
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Published in Economics Bulletin, 2015

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http://wp.economics.dal.ca/RePEc/dal/wpaper/DalEconWP2014-02.pdf (application/pdf)

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Journal Article: Learning to Agree: A New Perspective on Price Drift (2015)
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