English, Article, Journal or magazine article edition: Price Adjustment to News with Uncertain Precision Nikolaus Hautsch; Dieter Hess; Christoph Müller

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Bookmark: http://trove.nla.gov.au/version/178906
Physical Description
  • preprint
Language
  • English

Edition details

Title
  • Price Adjustment to News with Uncertain Precision
Author
  • Nikolaus Hautsch
  • Dieter Hess
  • Christoph Müller
Physical Description
  • preprint
Notes
  • prediction Bayesian learning; macroeconomic announcements; information quality; precision signals
  • RePEc:kud:kuiefr:200801
  • 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.
  • Bayesian learning, information quality, precision signals, macroeconomic announcements
  • RePEc:hum:wpaper:sfb649dp2008-025
  • 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.
  • RePEc:cfs:cfswop:wp200828
Language
  • English
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