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Price adjustment to news with uncertain precision

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  • Hautsch, Nikolaus
  • Hess, Dieter E.
  • Müller, Christoph

Abstract

Bayesian learning provides the core concept of processing noisy information. In standard Bayesian frameworks, assessing the price impact of information requires perfect knowledge of news' precision. In practice, however, precision is rarely dis- closed. Therefore, we extend standard Bayesian learning, suggesting traders infer news' precision from magnitudes of surprises and from external sources. We show that interactions of the different precision signals may result in highly nonlinear price responses. Empirical tests based on intra-day T-bond futures price reactions to employment releases confirm the model's predictions and show that the effects are statistically and economically significant.

Suggested Citation

  • Hautsch, Nikolaus & Hess, Dieter E. & Müller, Christoph, 2008. "Price adjustment to news with uncertain precision," CFS Working Paper Series 2008/28, Center for Financial Studies (CFS).
  • Handle: RePEc:zbw:cfswop:200828
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    References listed on IDEAS

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    Cited by:

    1. Hautsch, Nikolaus & Hess, Dieter & Veredas, David, 2011. "The impact of macroeconomic news on quote adjustments, noise, and informational volatility," Journal of Banking & Finance, Elsevier, vol. 35(10), pages 2733-2746, October.
    2. Hess, Dieter & Orbe, Sebastian, 2011. "Irrationality or efficiency of macroeconomic survey forecasts? Implications from the anchoring bias test," CFR Working Papers 11-13, University of Cologne, Centre for Financial Research (CFR).

    More about this item

    Keywords

    Bayesian Learning; Macroeconomic Announcements; Information Quality; Precision Signals;

    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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