Differential Interpretation of Information and the Post-Announcement Drift: A Story of Consensus Learning

Abstract:     
I show how a post-announcement drift can be generated in a model with fully rational investors who interpret public information differently. Differential interpretation of information transforms public raw information into private interpreted information. If investors recognize their limited ability to interpret information, they will look for other investors’ opinions in prices. Noise trading prevents investors from learning the market consensus interpretation of the announcement from the observation of a single price. But if noise trading follows a mean-reverting process, investors can gradually learn the market consensus from the observation of a series of prices. As investors become more confident about their interpretation of the announcement, they put more weight on it, and the announcement is gradually incorporated into prices, which generates a post-announcement drift. The model accounts for all salient empirical facts related to the post-announcement drift and delivers two new testable implications. If, in addition, investors make mistakes in extracting information from prices, the model also generates momentum.

Number of Pages in PDF File: 55

Keywords: post-announcement drift, momentum, differential interpretation, learning, explanation

JEL Classification: G11, G12, G14

working papers series