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.
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January 2002Abstract This paper delineates the simultaneous impact of non-anticipated information on mean and variance of the intraday return process by including appropriate variables accounting for the news flow into both the mean and the variance function. This allows us to differentiate between the consistent price reaction to surprising news and the traders' uncertainty about the precise price impact of this information. Focussing on the US employment report, we find that headline information is almost instantaneously incorporated into T-bond futures prices. Nevertheless, large surprises, and 'bad' news in particular, create considerable uncertainty. In contrast, if surprises in related headlines cross-validate each other, less room for differences of opinion is left, and hence volatility is decreased.
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