Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We analyze how markets adjust to new information when the reliability of news is uncertain and has to be estimated itself. We propose a Bayesian learning model where market participants receive fundamental information along with noisy estimates of news’ precision. It is shown that the efficiency of a precision estimate drives the slope and the shape of price response functions to news. Increasing estimation errors induce stronger nonlinearities in price responses. Analyzing high-frequency reactions of Treasury bond futures prices to employment releases, we find strong empirical support for the model’s predictions and show that the consideration of precision uncertainty is statistically and economically important.