We survey the nascent literature on machine learning in the study of financial markets. We highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This survey is designed for both financial economists interested in grasping...

I use nominal and real bond risks as new moments to discipline a New Keynesian asset pricing model, where supply shocks, demand shocks, and monetary policy are the fundamental drivers of inflation. Endogenously time-varying risk premia imply that nominal bond risks--as measured by their stock...

Empirical evidence shows that conditional market betas vary substantially over time. Yet, little is known about the source of this variation, either theoretically or empirically. Within a general equilibrium model with multiple assets and a time varying aggregate equity premium, we show that...

Missing data for return predictors is a common problem in cross sectional asset pricing. Most papers do not explicitly discuss how they deal with missing data but conventional treatments focus on the subset of firms with no missing data for any predictor or impute the unconditional mean. Both...

We theoretically characterize the behavior of machine learning asset pricing models. We prove that expected out-of-sample model performance--in terms of SDF Sharpe ratio and test asset pricing errors--is improving in model parameterization (or "complexity"). Our empirical findings verify the...

We use the forward-looking information from the US and global capital markets to estimate the economic impact of global warming, specifically, long-run temperature shifts. We find that global warming carries a positive risk premium that increases with the level of temperature and that has almost...

We propose a new measure of financial intermediary constraints based on how the intermediaries manage their tail risk exposures. Using data for the trading activities in the market of deep out-of-the-money S&P 500 put options, we identify periods when the variations in the net amount of trading...

We use traded equity dividend strips from U.S., Europe, and Japan from 2004-2017 to study the slope of the term structure of equity dividend risk premia. In the data, a robust finding is that the term structure of dividend risk premia (growth rates) is positively (negatively) sloped in...

This paper studies asset pricing in a setting in which idiosyncratic risk in human capital is not fully insurable. Firms use long-term contracts to provide insurance to workers, but neither side can commit to these contracts; furthermore, worker-firm relationships have endogenous durations owing...

We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premia. We demonstrate large economic gains to investors using machine learning forecasts, in some cases doubling the performance of leading regression-based...