Price adjustment to news with uncertain precision

Arbeitspapier

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: CFS Working Paper ; No. 2008/28

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Financial Markets and the Macroeconomy ; Information and Market Efficiency; Event Studies; Insider Trading
• Thema: Bayesian Learning ; Macroeconomic Announcements ; Information Quality ; Precision Signals ; Börsenkurs ; Anlageverhalten ; Informationsverhalten ; Lernprozess ; Wirtschaftsinformation ; Informationswert ; Ankündigungseffekt ; Theorie ; USA

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Hautsch, Nikolaus ; Hess, Dieter E. ; Müller, Christoph
• Ereignis: Veröffentlichung
• (wer): Goethe University Frankfurt, Center for Financial Studies (CFS)
• (wo): Frankfurt a. M.
• (wann): 2008

Weitere Informationen

• Handle: http://hdl.handle.net/10419/43278
• URN: urn:nbn:de:hebis:30-57665
• Letzte Aktualisierung: 10.03.2025, 11:43 MEZ