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

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  • Hautsch, Nikolaus
  • Hess, Dieter

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.

Suggested Citation

  • Hautsch, Nikolaus & Hess, Dieter, 2007. "Bayesian Learning in Financial Markets: Testing for the Relevance of Information Precision in Price Discovery," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 42(1), pages 189-208, March.
    • Handle: RePEc:cup:jfinqa:v:42:y:2007:i:01:p:189-208_00
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    More about this item

    JEL classification:

    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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