Our objective is to identify the trading strategy that would allow an investor to take advantage of excessive stock price volatility and sentiment fluctuations. We construct a general-equilibrium model of sentiment. In it, there are two classes of agents and stock prices are excessively volatile...

Our objective is to understand the trading strategy that would allow an investor to take advantage of quot;excessivequot; stock price volatility and quot;sentimentquot; fluctuations. We construct a general equilibrium model of sentiment. In it, there are two classes of agents and stock prices...

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 propose that investment strategies should be evaluated based on their net-of-trading-cost return for each level of risk, which we term the "implementable efficient frontier." While numerous studies use machine learning return forecasts to generate portfolios, their agnosticism toward trading...

We investigate the performance of non-linear return prediction models in the high complexity regime, i.e., when the number of model parameters exceeds the number of observations. We document a "virtue of complexity" in all asset classes that we study (US equities, international equities, bonds,...