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...

We study optimal experimentation by a monopolistic platform in a two-sided market framework. The platform provider faces uncertainty about the strength of the externality each side is exerting on the other. It maximizes the expected present value of its profit stream in a continuous-time...

We study optimal experimentation by a monopolistic platform in a two-sided mar- ket. The platform provider faces uncertainty about the strength of the externality each side is exerting on the other. It maximizes the expected present value of its profit stream in a continuous-time...

We study optimal experimentation by a monopolistic platform in a two-sided market. The platform provider is uncertain about the strength of the externality each side is exerting on the other. Setting participation fees on both sides, it gradually learns about these externalities by observing...

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 optimal experimentation by a monopolistic platform in a two-sided market framework. The platform provider faces uncertainty about the strength of the externality each side is exerting on the other. It maximizes the expected present value of its profit stream in a continuous-time...

We study optimal experimentation by a monopolistic platform in a two-sided market. The platform provider is uncertain about the strength of the externality each side is exerting on the other. Setting participation fees on both sides, it gradually learns about these externalities by observing...

We study optimal experimentation by a monopolistic platform in a two-sided mar- ket. The platform provider faces uncertainty about the strength of the externality each side is exerting on the other. It maximizes the expected present value of its profit stream in a continuous-time...

Bayesian learning provides a core concept of information processing in financial markets. Typically it is assumed that market participants perfectly know the quality of released news. However, in practice, news’ precision is rarely disclosed. Therefore, we extend standard Bayesian learning...