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Price Adjustment to News with Uncertain Precision

Nikolaus Hautsch (), Dieter Hess and Christoph Müller

SFB 649 Discussion Papers from Humboldt University, Collaborative Research Center 649

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.

Keywords: Bayesian learning; information quality; precision signals; macroeconomic announcements (search for similar items in EconPapers)
JEL-codes: E44 G14 (search for similar items in EconPapers)
Pages: 47 pages
Date: 2008-03
New Economics Papers: this item is included in nep-cba, nep-mac and nep-mst
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Citations: View citations in EconPapers (4) Track citations by RSS feed

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Related works:
Journal Article: Price adjustment to news with uncertain precision (2012) Downloads
Working Paper: Price adjustment to news with uncertain precision (2011) Downloads
Working Paper: Price Adjustment to News with Uncertain Precision (2008) Downloads
Working Paper: Price adjustment to news with uncertain precision (2008)
Working Paper: Price adjustment to news with uncertain precision (2008) Downloads
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