Predicting Future Earnings Changes Using Machine Learning and Detailed Financial Data

• 成果类型: Article
• 署名作者: Chen, Xi; Cho, Yang Ha (Tony); Dou, Yiwei; Lev, Baruch
• 署名单位: New York University; New York University
• 刊物名称: JOURNAL OF ACCOUNTING RESEARCH
• ISSN/ISSBN: 0021-8456
• DOI: 10.1111/1475-679X.12429
• 发表日期: 2022
• 页码: 467-515
• 关键词: fundamental analysis; xbrl disclosures; information-content; accounting numbers; statement analysis; DELISTING BIAS; FILINGS; QUALITY; IMPACT; cost

摘要

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 ranges from 67.52% to 68.66%, significantly higher than the 50% 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%. 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.