A common practice of banks has been to pool assets of different qualities and then sell a fraction of the newly created portfolios to investors. We extend the signaling model for single sales of risky assets to portfolio sales. We identify conditions under which signaling at the portfolio level...

We consider a privately informed issuer which holds a portfolio of assets that can be sold to raise cash, where the fractions of assets sold serve as a multidimensional signal. If good news about one asset is good news for the others, then there is a unique equilibrium that satisfies the...

By generalizing the Leland and Pyle (1977) model to the case of multiple correlated assets, this paper studies the signaling and hedging behavior of an intermediary who sells multiple assets in financial markets. Based on information asymmetry, this paper demonstrates the intrinsic...

A rational-expectations equilibrium with positive demand for financial information does exist under fully revealing asset price - contrary to a wide-held conjecture. Generalizing the common additive signal-return model with CARA utility to the family of distributions with moment generating...

In this study, we employ a statistical arbitrage approach to demonstrate that momentum investment strategy tend to work better in periods longer than six months, a result different from findings in past literature. Compared with standard parametric tests, the statistical arbitrage method...

I present a dynamic investment model in which mutual funds' inferior performance is an equilibrium response to incentives rather than the consequence of low skills. In the model, a skilled (informed) manager responds to investors' flows, which are a convex function of performance relative to...

We show that benchmark-linked convex incentives can lead risk-averse money managers aware of mispricing to over-invest in overpriced securities. In the model, the managers' risk-seeking behavior varies in response to the interaction of mispricing with convexity and benchmarking concerns....