Bayesian learning in financial markets: Testing for the relevance of information precision in price discovery
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.
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- Gerald P. Dwyer & R. W. Hafer, 1989. "Interest rates and economic announcements," Review, Federal Reserve Bank of St. Louis, issue Mar, pages 34-46.
- 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.
- Engle, Robert F., 1982. "A general approach to lagrange multiplier model diagnostics," Journal of Econometrics, Elsevier, vol. 20(1), pages 83-104, October.
- 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.
- Gadi Barlevy & Pietro Veronesi, 2000. "Information Acquisition in Financial Markets," Review of Economic Studies, Oxford University Press, vol. 67(1), pages 79-90.
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