Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better...

This paper uses a comprehensive set of variables from the five largest Eurozone countries to compare the performance of simple univariate and machine learning-based multivariate models in predicting stock market crashes. The statistical predictive performance of a support vector machine-based...

Machine learning offers a set of powerful tools that holds considerable promise for investment management. As with most quantitative applications in finance, the danger of misapplying these techniques can lead to disappointment. One crucial limitation involves data availability. Many of machine...

The emergence of algorithmic high-frequency trading in the market for credit risk affords accurate inference of new risk measures. When combined with machine learning predictive methods, these measures forecast substantial future changes in firms' credit and equity risk premiums in...

Conducting, to our knowledge, the largest study ever of five-minute equity market returns using state-of-the-art machine learning models trained on the cross-section of lagged market index constituent returns, we show that regularized linear models and nonlinear tree-based models yield...

This paper proposes a machine learning approach to building investment strategies that addresses several drawbacks of a classic approach. To demonstrate our approach, we use a logistic regression algorithm to build a time-series dual momentum trading strategy on the S&P 500 Index. Our algorithm...

We apply state-of-the-art Bayesian machine learning to test whether we can extract valuable information from analysts' recommendations of stock performance. We use a probabilistic model for independent Bayesian classifier combination that has been successfully applied in both the physical and...

This paper reviews research that uses big data and/or machine learning methods to provide insight relevant for equity valuation. Given the huge volume of research in this area, the review focuses on studies that either use or inform on accounting variables. The article concludes by providing...

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 core statistical technology in artificial intelligence is the large-scale transformer network. We propose a new asset pricing model that implants a transformer in the stochastic discount factor. This structure leverages conditional pricing information via cross-asset information sharing and...