Predicting Future Earnings Changes Using Machine Learning and Detailed Financial Data
使用机器学习方法和详细的财务数据预测一年后盈利变化方向，模型在样本外预测能力显著优于传统逻辑回归和财务分析师预测，基于预测构建的对冲组合年化收益达5.02%至9.74%。
ABSTRACT 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.