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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- Gadi Barlevy & Pietro Veronesi, 2000.
"Information Acquisition in Financial Markets,"
Review of Economic Studies,
Oxford University Press, vol. 67(1), pages 79-90.
- Gadi Barlevy & Pietro Veronesi, "undated". "Information Acquisition in Financial Markets," CRSP working papers 360, Center for Research in Security Prices, Graduate School of Business, University of Chicago.
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- 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. Full references (including those not matched with items on IDEAS)
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