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...

Income is simultaneously one of the most important variables used by economists and the variable most likely to be missing due to item non-response. While observations that are missing income responses are often dropped from analyses, such treatment is usually inappropriate. More appropriate...

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...

This study analyzes the impact of major industrial relations variables on productivity within a plant that assembles large commercial aircraft. The analysis combines the deep firm- specific knowledge of management and labor typical of the best of traditional industrial relations with formal...

This paper reports the results of a field study examining the use of TQM at 15 firms. The sample is drawn from winners and finalists of the RIT/USA Today Quality Cup. The authors interviewed 75 employees (5 per firm) including 14 executives, 44 middle managers, and 17 front line workers. The...