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

Nikolaus Hautsch () and Dieter Hess
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Dieter Hess: University of Cologne

No 04-17, Discussion Papers from University of Copenhagen. Department of Economics

Abstract: An important claim of Bayesian learning and a standard assumption in price discovery models is that the strength of the price impact of unanticipated information depends on the precision of the news. In this paper, we test for this assumption by analyzing intra-day price responses of CBOT T-bond futures to U.S. employment announcements. By employing additional detail information besides the widely used headline figures, we extract release-specific precision measures which allow to test for the claim of Bayesian updating. We find that the price impact of more precise information is significantly stronger. The results remain stable even after controlling for an asymmetric price response to 'good' and 'bad' news.

Keywords: Bayesian learning; information precision; macroeconomic announcements; asymmetric price response; financial markets; high-frequency data (search for similar items in EconPapers)
JEL-codes: E44 G14 (search for similar items in EconPapers)
Date: Written
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Related works:
Working Paper: Bayesian Learning in Financial Markets – Testing for the Relevance of Information Precision in Price Discovery (2004)
Journal Article: Bayesian Learning in Financial Markets: Testing for the Relevance of Information Precision in Price Discovery (2007)
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