Abstract 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. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Firm volatilities comove strongly over time, and their common factor is the dispersion of the economy-wide firm size distribution. In the cross section, smaller firms and firms with a more concentrated customer base display higher volatility. Network effects are essential to explaining the joint evolution of the empirical firm size and firm volatility distributions. We propose and estimate a simple network model of firm volatility in which shocks to customers influence their suppliers. Larger suppliers have more customers, and customer-supplier links depend on customers’ size. The model produces distributions of firm volatility, size, and customer concentration consistent with the data.

We study how sophisticated investors, when faced with shocks to information environment, change their information acquisition and trading behavior, and how these changes in turn affect market efficiency. We find that, after exogenous reductions of analyst coverage due to closures and mergers of brokerage firms, hedge funds scale up information acquisition, trade more aggressively, and earn higher abnormal returns on the affected stocks. The hedge fund participation also mitigates the impairment of market efficiency caused by coverage reductions. Overall, in a causal framework, our findings suggest a substitution effect between sophisticated investors and public information providers in facilitating market efficiency.

Abstract 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.