We propose a new asset-pricing framework in which all securities' signals are used to predict each individual return. While the literature focuses on each security's own-signal predictability, assuming an equal strength across securities, our framework is flexible and includes...

We study the general problem of Bayesian persuasion (optimal information design) with continuous actions and continuous state space in arbitrary dimensions. First, we show that with a finite signal space, the optimal information design is always given by a partition. Second, we take the limit of...

We consider an economy populated by CARA investors who trade, accounting for their price impact, multiple risky assets with arbitrary distributed payoffs. We propose a constructive solution method: finding the equilibrium reduces to solving a linear ordinary differential equation. With market...

We develop a rational model of trading behavior in which the agents gradually learn about their ability to trade, and exit after poor trading performance. We demonstrate that it is optimal for experienced traders to "procrastinate" and postpone exit even after bad results. We embed this "optimal...

We consider a market where traders have asymmetric information regarding the distribution of asset return and study price discovery of derivatives. The informed trader has private information regarding arbitrary higher moments of asset return, such as volatility or skewness, and exploits her...

We propose a new asset-pricing framework in which all securities' signals are used to predict each individual return. While the literature focuses on each security's own- signal predictability, assuming an equal strength across securities, our framework is flexible and includes...

We introduce intermediation frictions into the classical monetary model with fully flexible prices. Trade in financial assets occurs through intermediaries who bargain over a full set of state-contingent claims with their customers. Monetary policy is redistributive and affects intermediaries'...