Price adjustment to news with uncertain precision

Bayesian learning provides the core concept of information processing in financial markets. Typically, it is assumed that market participants know perfectly the quality of released news. However, in practice, news' precision is rarely disclosed. Therefore, we extend standard Bayesian learning, suggesting traders infer news' precision from two different sources. If information is perceived to be more precise, prices react more strongly. Moreover, interactions of the different precision signals affect price responses nonlinearly. Empirical tests based on intra-day T-bond futures price reactions to employment releases confirm the model's predictions and reveal statistically and economically significant effects of news' precision.