TY - UNPD A1 - Hautsch, Nikolaus A1 - Hess, Dieter A1 - Müller, Christoph T1 - Price adjustment to news with uncertain precision T2 - Center for Financial Studies (Frankfurt am Main): CFS working paper series ; No. 2008,28 N2 - Bayesian learning provides the core concept of processing noisy information. In standard Bayesian frameworks, assessing the price impact of information requires perfect knowledge of news’ precision. In practice, however, precision is rarely dis- closed. Therefore, we extend standard Bayesian learning, suggesting traders infer news’ precision from magnitudes of surprises and from external sources. We show that interactions of the different precision signals may result in highly nonlinear price responses. Empirical tests based on intra-day T-bond futures price reactions to employment releases confirm the model’s predictions and show that the effects are statistically and economically significant. T3 - CFS working paper series - 2008, 28 Y1 - 2008 UR - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/5828 UR - https://nbn-resolving.org/urn:nbn:de:hebis:30-57665 ER -