[We provide evidence for the importance of information asymmetry in asset pricing by using three natural experiments. Consistent with rational expectations models with multiple assets and multiple signals, we find that prices and uninformed demand fall as asymmetry increases. These falls are larger when more investors are uninformed, turnover is larger and more variable, payoffs are more uncertain, and the lost signal is more precise. Prices fall partly because expected returns become more sensitive to liquidity risk. Our results confirm that information asymmetry is priced and imply that a primary channel that links asymmetry to prices is liquidity.]

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 costs leads to excessive reliance on fleeting small-scale characteristics, resulting in poor net returns. We develop a framework that produces a superior frontier by integrating trading-cost-aware portfolio optimization with machine learning. The superior net-of-cost performance is achieved by learning directly about portfolio weights using an economic objective. Further, our model gives rise to a new measure of “economic feature importance.”

We estimate a narrative factor pricing model from news text of The Wall Street Journal. Our empirical method integrates topic modeling (LDA), latent factor analysis (IPCA), and variable selection (group lasso). Narrative factors achieve higher out-of-sample Sharpe ratios and smaller pricing errors than standard characteristic-based factor models and predict future investment opportunities in a manner consistent with the ICAPM. We derive an interpretation of the estimated risk factors from narratives in the underlying article text.

We propose a new measure of time-varying tail risk that is directly estimable from the cross-section of returns. We exploit firm-level price crashes every month to identify common fluctuations in tail risk among individual stocks. Our tail measure is significantly correlated with tail risk measures extracted from S&P 500 index options and negatively predicts real economic activity. We show that tail risk has strong predictive power for aggregate market returns. Cross-sectionally, stocks with high loadings on past tail risk earn an annual three-factor alpha 5.4% higher than stocks with low tail risk loadings. We explore potential mechanisms giving rise to these asset pricing facts.

We provide evidence for the importance of information asymmetry in asset pricing by using three natural experiments. Consistent with rational expectations models with multiple assets and multiple signals, we find that prices and uninformed demand fall as asymmetry increases. These falls are larger when more investors are uninformed, turnover is larger and more variable, payoffs are more uncertain, and the lost signal is more precise. Prices fall partly because expected returns become more sensitive to liquidity risk. Our results confirm that information asymmetry is priced and imply that a primary channel that links asymmetry to prices is liquidity.

We propose a statistical model of heterogeneous beliefs wherein investors are represented as different machine learning model specifications. Investors form return forecasts from their individual models using common data inputs. We measure disagreement as forecast dispersion across investor-models (MFD). Our measure aligns with analyst forecast disagreement but more powerfully predicts returns. We document a large and robust association between belief disagreement and future returns. A decile spread portfolio that sells stocks with high disagreement and buys stocks with low disagreement earns a value-weighted return of 13% per year. Further analyses suggest MFD-alpha is mispricing induced by short-sale costs and limits-to-arbitrage.

We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premiums. We demonstrate large economic gains to investors using machine learning forecasts, in some cases doubling the performance of leading regression-based strategies from the literature. We identify the best-performing methods (trees and neural networks) and trace their predictive gains to allowing nonlinear predictor interactions missed by other methods. All methods agree on the same set of dominant predictive signals, a set that includes variations on momentum, liquidity, and volatility.

We propose and implement a procedure to dynamically hedge climate change risk. We extract innovations from climate news series that we construct through textual analysis of newspapers. We then use a mimicking portfolio approach to build climate change hedge portfolios. We discipline the exercise by using third-party ESG scores of firms to model their climate risk exposures. We show that this approach yields parsimonious and industry-balanced portfolios that perform well in hedging innovations in climate news both in sample and out of sample. We discuss multiple directions for future research on financial approaches to managing climate risk.