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Bayesian Learning in Financial Markets: Testing for the Relevance of Information Precision in Price Discovery

Published online by Cambridge University Press:  06 April 2009

Nikolaus Hautsch  and
Dieter Hess
Nikolaus Hautsch
Affiliation:
nikolaus.hautsch@wiwi.hu-berlin.de, Humboldt-Universität zu Berlin, Department of Economics, Spandauer Str. 1, D-10178, Berlin, Germany
Dieter Hess
Affiliation:
hess@wiso.uni-koeln.de, University of Cologne, Corporate Finance Seminar and Centre for Financial Research, Albert-Magnus-Platz, D-50923, Cologne, Germany
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Abstract

Bayesian learning claims that the strength of the price impact of unanticipated information depends on the relative precision of traders' prior and posterior beliefs. In this paper, we test for this implication of Bayesian models by analyzing intraday price responses of T-bond futures to U.S. employment announcements. By employing additional detailed information in addition to the widely used headline figures, we extract release-specific precision measures. We find that the price impact of more precise information is significantly stronger, even after controlling for an asymmetric price response to “good” and “bad” news. This result strengthens previous findings that differences in earnings response coefficients across companies are related to proxies for the credibility of the reported financial information.

Information

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 42 , Issue 1 , March 2007 , pp. 189 - 208
Copyright
Copyright © School of Business Administration, University of Washington 2007

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