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Bayesian learning in financial markets: Testing for the relevance of information precision in price discovery


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  • Hautsch, Nikolaus
  • Hess, Dieter


An important claim of Bayesian learning and a standard assumption in price discovery models is that the strength of the price impact of unanticipated information depends on the precision of the news. In this paper, we test for this assumption by analyzing intra-day price responses of CBOT T-bond futures to U.S. employment announcements. By employing additional detail information besides the widely used headline figures, we extract release-specific precision measures which allow to test for the claim of Bayesian updating. We find that the price impact of more precise information is significantly stronger. The results remain stable even after controlling for an asymmetric price response to 'good' and 'bad' news. -- Preise auf Kapitalmärkten werden durch nicht-antizipierte Informationen getrieben. Eine zentrale Aussage Baysianischer Lernmodelle impliziert, dass die Stärke der Preisreaktion einerseits von der Höhe der nicht antizipierten Komponente abhängt, andererseits aber auch von der Präzision der Informationen bestimmt wird. Auf Basis von Hochfrequenzdaten untersuchen wir den Einfluss von Veröffentlichungen des US-Arbeitsmarktberichts auf den Preis des CBOT T-Bond Futures. Neben den Kernkennzahlen des US-Arbeitsmarktberichts berücksichtigen wir ergänzende Detailinformationen. Mit Hilfe dieser Zusatzinformationen konstruieren wir ein Maß für die Präzision der Kernkennzahlen, welches uns erlaubt, die Aussage Baysianischer Lernmodelle zu testen. Wir finden, dass die Preise des T-Bond Futures stärker auf präzisere Informationen reagieren. Dieses Ergebnis gilt insbesondere auch dann, wenn wir für asymmetrische Preisreaktionen bezüglich positiver bzw. negativer Informationen kontrollieren.

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Bibliographic Info

Paper provided by University of Cologne, Centre for Financial Research (CFR) in its series CFR Working Papers with number 04-10.

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Date of creation: 2004
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Handle: RePEc:zbw:cfrwps:0410

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Keywords: Bayesian learning; information precision; macroeconomic announcements; asymmetric price response; financial markets; high-frequency data;

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  1. Engle, Robert F, 1998. "Macroeconomic Announcements and Volatility of Treasury Futures," University of California at San Diego, Economics Working Paper Series, Department of Economics, UC San Diego qt7rd4g3bk, Department of Economics, UC San Diego.
  2. Gadi Barlevy & Pietro Veronesi, . "Information Acquisition in Financial Markets," CRSP working papers 484, Center for Research in Security Prices, Graduate School of Business, University of Chicago.
  3. Engle, Robert F., 1982. "A general approach to lagrange multiplier model diagnostics," Journal of Econometrics, Elsevier, Elsevier, vol. 20(1), pages 83-104, October.
  4. Gerald P. Dwyer, Jr. & R.W. Hafer, 1989. "Interest rates and economic announcements," Review, Federal Reserve Bank of St. Louis, issue Mar, pages 34-46.
  5. Michael J. Fleming & Eli M. Remolona, 1999. "Price Formation and Liquidity in the U.S. Treasury Market: The Response to Public Information," Journal of Finance, American Finance Association, American Finance Association, vol. 54(5), pages 1901-1915, October.
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