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Price Adjustment to News with Uncertain Precision

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  • Nikolaus Hautsch
  • Dieter Hess
  • Christoph Müller

Abstract

Bayesian learning provides a core concept of information processing in financial markets. Typically it is assumed that market participants perfectly know the quality of released news. However, in practice, news’ precision is rarely disclosed. Therefore, we extend standard Bayesian learning allowing traders to infer news’ precision from two different sources. If information is perceived to be imprecise, prices react stronger. Moreover, interactions of the different precision signals affect price responses nonlinearly. Empirical tests based on intra-day T-bond futures price reactions to employment releases confirm the model’s predictions and reveal statistically and economically significant effects of news’ precision.

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Bibliographic Info

Paper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number SFB649DP2008-025.

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Length: 47 pages
Date of creation: Mar 2008
Date of revision:
Handle: RePEc:hum:wpaper:sfb649dp2008-025

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Keywords: Bayesian learning; information quality; precision signals; macroeconomic announcements;

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References

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Cited by:
  1. Hautsch, Nikolaus & Hess, Dieter E. & Veredas, David, 2010. "The impact of macroeconomic news on quote adjustments, noise, and informational volatility," CFS Working Paper Series, Center for Financial Studies (CFS) 2010/01, Center for Financial Studies (CFS).
  2. Hess, Dieter & Orbe, Sebastian, 2011. "Irrationality or efficiency of macroeconomic survey forecasts? Implications from the anchoring bias test," CFR Working Papers, University of Cologne, Centre for Financial Research (CFR) 11-13, University of Cologne, Centre for Financial Research (CFR).

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