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 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 United Nations Human Development Index (HDI) is arguably the most widely used alternative to gross domestic product for measuring national development. This is in large part due to its multidimensional nature, as it incorporates not only income, but also education and health. However, the...

Are speculators driving up oil prices? Should we raise energy prices to slow global warming? The present study takes a small number of such questions and compares the views of economic experts with those of the public. This comparison uses a panel of more than 2000 respondents from YouGov with...

We study the distribution of political speech across U.S. firms. We develop a measure of political engagement based on firms' communications (earnings calls, regulatory filings, and social media), by training a large language model to identify statements that contain political opinions. Using...

Primary historical sources are often by-passed for secondary sources due to high human costs of accessing and extracting primary information-especially in lower-resource settings. We propose a supervised machine-learning approach to the natural language processing of Chinese historical data. An...

Expanding on an approach suggested by Ashenfelter (1984), we extend the Phillips curve to an open economy and exploit panel data to estimate the textbook 'expectations augmented' Phillips curve with a market-based and observable measure of inflation expectations. We develop this measure using...

The synthetic control method (SCM) is a popular approach for estimating the impact of a treatment on a single unit in panel data settings. The "synthetic control" is a weighted average of control units that balances the treated unit's pre-treatment outcomes and other covariates as closely as...