There is strong evidence that macroeconomic releases influence prices in financial markets. However, why do markets react to some announcements while they ignore others with a similar content? Based on a Bayesian learning model, we show that market impact is mainly determined by information...

Analysts providing more accurate earnings forecasts also issue more profitable recommendations. We demonstrate how investors can profit from this contemporaneous link by differentiating between able and lucky analysts. In line with previous studies, we find that past track records alone are not...

Standard equity valuation approaches (i.e., DDM, RIM, and DCF model) are derived under the assumption of ideal conditions, such as infinite payoffs and clean surplus accounting. Because these conditions are hardly ever met, we extend the standard approaches, based on the fundamental principle of...

We analyze the quality of macroeconomic survey forecasts. Recent findings indicate that they are anchoring biased. This irrationality would challenge the results of a wide range of empirical studies, e.g., in asset pricing, volatility clustering or market liquidity, which rely on survey data to...

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...

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...