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...

We study the impact of the arrival of macroeconomic news on the informational and noise-driven components in high-frequency quote processes and their conditional variances. Bid and ask returns are decomposed into a common ("efficient return") factor and two market-side-specific components...

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,...

This paper is an investigation into the determinants of asymmetries in stock returns. We develop a series of cross-sectional regression specifications which attempt to forecast skewness in the daily returns of individual stocks. Negative skewness is most pronounced in stocks that have...

This paper reports empirical tests for the existence of rational bubbles in stock prices. The analysis focuses on a familiar model that defines market fundamentals to be the expected present value of dividends, discounted at a constantrate, and defines a rational bubble to be a self-confirming...

The solution to a linear model in which supply and/or demand depends on rational expectations of future prices can involve three parts, which we denote as the fundamental component, the deterministic bubble component, and the stochastic bubble component. This paper explores the properties of...

We study the implications of learning in an environment where the true model of the world is a multivariate one, but where agents update only over the class of simple univariate models. If a particular simple model does a poor job of forecasting over a period of time, it is eventually discarded...

We assume that the instantaneous riskless rate reverts towards a central tendency which in turn, is changing stochastically over time. As a result, current short-term rates are not" sufficient to predict future short-term rates movements, as would be the case if the central" tendency was...