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...

We propose a statistical model of differences in beliefs in which heterogeneous investors are represented as different machine learning model specifications. Each investor forms return forecasts from their own specific model using data inputs that are available to all investors. We measure...

We measure investors' short- and long-term stock-return expectations using both options and survey data. These expectations at different horizons reveal what investors think their own short-term expectations will be in the future, or forward return expectations. While contemporaneous short-term...

The extant literature predicts market returns with "simple" models that use only a few parameters. Contrary to conventional wisdom, we theoretically prove that simple models severely understate return predictability compared to "complex" models in which the number of parameters exceeds the...

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...

The amount of information produced about firms' productivities and about the quality of collateral backing their loans varies over time. These information dynamics determine the evolution of credit, output and productivity, which feeds back into incentives to produce information. We characterize...

We introduce and analyze a new market design for trading financial assets. The design allows traders to directly trade any user-defined linear combination of assets. Orders for such portfolios are expressed as downward-sloping piecewise-linear demand curves with quantities as flows...

We introduce FDIF, a measure of Fed communication surprise based on the text of FOMC statements. FDIF measures the difference between text-implied and actual values of key market variables. Positive FDIF of countercyclical variables (e.g., credit spreads) is associated with negative...

General equilibrium analysis is difficult when asset markets are incomplete. We make the simplifying assumption that uncertainty is small and use bifurcation methods to compute Taylor series approximations for asset demand and asset market equilibrium. A computer must be used to derive these...

Why did the real interest rate decline and the equity premium increase over the last 30 years? This paper assesses the role of uncertainty and credit market frictions. We quantify a model with heterogeneous households using data on asset prices and macro aggregates, as well as on households'...