Bayesian Learning in Financial Markets: Testing for the Relevance of Information Precision in Price Discovery
Bayesian learning claims that the strength of the price impact of unanticipated information depends on the relative precision of traders' prior and posterior beliefs. In this paper, we test for this implication of Bayesian models by analyzing intraday price responses of T-bond futures to U.S. employment announcements. By employing additional detailed information in addition to the widely used headline figures, we extract release-specific precision measures. We find that the price impact of more precise information is significantly stronger, even after controlling for an asymmetric price response to “good” and “bad” news. This result strengthens previous findings that differences in earnings response coefficients across companies are related to proxies for the credibility of the reported financial information.
Volume (Year): 42 (2007)
Issue (Month): 01 (March)
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- Gadi Barlevy & Pietro Veronesi, .
"Information Acquisition in Financial Markets,"
CRSP working papers
360, Center for Research in Security Prices, Graduate School of Business, University of Chicago.
- Engle, Robert F., 1982. "A general approach to lagrange multiplier model diagnostics," Journal of Econometrics, Elsevier, vol. 20(1), pages 83-104, October.
- 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.
- 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.
- 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.
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