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Bayesian Learning in Financial Markets – Testing for the Relevance of Information Precision in Price Discovery


  • Nikolaus Hautsch

    (Institute of Economics, University of Copenhagen)

  • Dieter Hess

    (University of Cologne)


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.

Suggested Citation

  • Nikolaus Hautsch & Dieter Hess, 2004. "Bayesian Learning in Financial Markets – Testing for the Relevance of Information Precision in Price Discovery," FRU Working Papers 2004/06, University of Copenhagen. Department of Economics. Finance Research Unit.
  • Handle: RePEc:kud:kuiefr:200406

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    References listed on IDEAS

    1. Gadi Barlevy & Pietro Veronesi, 2000. "Information Acquisition in Financial Markets," Review of Economic Studies, Oxford University Press, vol. 67(1), pages 79-90.
    2. Engle, Robert F., 1982. "A general approach to lagrange multiplier model diagnostics," Journal of Econometrics, Elsevier, vol. 20(1), pages 83-104, October.
    3. Gerald P. Dwyer & R. W. Hafer, 1989. "Interest rates and economic announcements," Review, Federal Reserve Bank of St. Louis, issue Mar, pages 34-46.
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    5. Engle, Robert F, 1998. "Macroeconomic Announcements and Volatility of Treasury Futures," University of California at San Diego, Economics Working Paper Series qt7rd4g3bk, Department of Economics, UC San Diego.
    6. 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, vol. 54(5), pages 1901-1915, October.
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    Cited by:

    1. Markku Lanne, 2009. "Properties of Market-Based and Survey Macroeconomic Forecasts for Different Data Releases," Economics Bulletin, AccessEcon, vol. 29(3), pages 2231-2240.
    2. Hautsch, Nikolaus & Hess, Dieter & Veredas, David, 2011. "The impact of macroeconomic news on quote adjustments, noise, and informational volatility," Journal of Banking & Finance, Elsevier, vol. 35(10), pages 2733-2746, October.
    3. A. A. Brown & L. C. G. Rogers, 2009. "Heterogeneous Beliefs with Finite-Lived Agents," Papers 0907.4953,
    4. Jaehun CHOI & Hosung LIM & Rogelio Jr. MERCADO & Cyn-Young PARK, 2015. "Price Discovery and Foreign Participation in Korea's Government Bond Cash and Futures Markets," Working Papers 2015-8, Economic Research Institute, Bank of Korea.
    5. Alexandra Niessen, 2007. "Media Coverage and Macroeconomic Information Processing," SFB 649 Discussion Papers SFB649DP2007-011, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    6. Menkhoff, Lukas & Schmeling, Maik, 2010. "Whose trades convey information? Evidence from a cross-section of traders," Journal of Financial Markets, Elsevier, vol. 13(1), pages 101-128, February.
    7. Hautsch, Nikolaus & Hess, Dieter & Müller, Christoph, 2012. "Price adjustment to news with uncertain precision," Journal of International Money and Finance, Elsevier, vol. 31(2), pages 337-355.
    8. Gilbert, Thomas, 2011. "Information aggregation around macroeconomic announcements: Revisions matter," Journal of Financial Economics, Elsevier, vol. 101(1), pages 114-131, July.
    9. Huotari, Jarkko, 2015. "Measuring financial stress – A country specific stress index for Finland," Research Discussion Papers 7/2015, Bank of Finland.
    10. Thomas Gilbert & Chiara Scotti & Georg H. Strasser & Clara Vega, 2015. "Is the Intrinsic Value of Macroeconomic News Announcements Related to Their Asset Price Impact?," Boston College Working Papers in Economics 874, Boston College Department of Economics, revised 23 Apr 2015.
    11. Isengildina-Massa, Olga & Irwin, Scott H. & Good, Darrel L., 2010. "Quantile Regression Estimates of Confidence Intervals for WASDE Price Forecasts," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 35(3), December.
    12. Fricke, Christoph & Menkhoff, Lukas, 2011. "Does the "Bund" dominate price discovery in Euro bond futures? Examining information shares," Journal of Banking & Finance, Elsevier, vol. 35(5), pages 1057-1072, May.
    13. Linda S. Goldberg & Christian Grisse, 2013. "Time variation in asset price responses to macro announcements," Staff Reports 626, Federal Reserve Bank of New York.
    14. Withanawasam, R.M. & Whigham, P.A. & Crack, T.F., 2013. "Characterising trader manipulation in a limit-order driven market," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 93(C), pages 43-52.
    15. Laakkonen, Helinä & Lanne, Markku, 2009. "The Relevance of Accuracy for the Impact of Macroeconomic News on Volatility," MPRA Paper 23718, University Library of Munich, Germany.
    16. Marcello Pericoli & Giovanni Veronese, 2015. "Forecaster heterogeneity, surprises and financial markets," Temi di discussione (Economic working papers) 1020, Bank of Italy, Economic Research and International Relations Area.
    17. Grothe, Magdalena, 2010. "Price and trading response to public information," Working Paper Series 1177, European Central Bank.
    18. Laivi Laidroo & Zana Grigaliuniene, 2012. "Testing for asymmetries in price reactions to quarterly earnings announcements on Tallinn, Riga and Vilnius Stock Exchanges during 2000-2009," Baltic Journal of Economics, Baltic International Centre for Economic Policy Studies, vol. 12(1), pages 61-86, July.

    More about this item


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

    JEL classification:

    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading


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