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

Hess, Dieter; Hautsch, Nikolaus

doi:10.2139/ssrn.741566

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

Centre for Financial Research (CFR), Working Paper 04-10

37 Pages Posted: 14 Jun 2005 Last revised: 16 Sep 2010

See all articles by Dieter Hess

Dieter Hess

University of Cologne - Department of Corporate Finance; University of Cologne - Centre for Financial Research (CFR)

Nikolaus Hautsch

University of Vienna - Department of Statistics and Operations Research; Center for Financial Studies (CFS); Vienna Graduate School of Finance (VGSF)

There are 2 versions of this paper

Date Written: 2004

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

JEL Classification: E44, G14

Suggested Citation: Suggested Citation

Hess, Dieter and Hautsch, Nikolaus, Bayesian Learning in Financial Markets - Testing for the Relevance of Information Precision in Price Discovery (2004). Centre for Financial Research (CFR), Working Paper 04-10. Available at SSRN: https://ssrn.com/abstract=741566 or http://dx.doi.org/10.2139/ssrn.741566