Predicting Future Earnings Changes Using Machine Learning and Detailed Financial Data
Journal of Accounting Research, Forthcoming
91 Pages Posted: 3 Feb 2021 Last revised: 14 Feb 2022
Date Written: February 12, 2022
We use machine learning methods and high-dimensional detailed financial data to predict the direction of one-year-ahead earnings changes. Our models show significant out-of-sample predictive power: the area under the Receiver Operating Characteristics curve (AUC) ranges from 67.52 to 68.66 percent, significantly higher than the 50 percent of a random guess. The annual size-adjusted returns to hedge portfolios formed based on the prediction of our models range from 5.02 to 9.74 percent. Our models outperform two conventional models that use logistic regressions and small sets of accounting variables, and professional analysts’ forecasts. Analyses suggest that the outperformance relative to the conventional models stems from both nonlinear predictor interactions missed by regressions and the use of more detailed financial data by machine learning.
Keywords: Direction of Earnings Changes; Prediction; Detailed Financial Data; XBRL; Machine Learning
JEL Classification: C53; G12; G17; M41
Suggested Citation: Suggested Citation