Fundamental Analysis via Machine Learning
研究了机器学习在预测公司盈利这一基本面分析核心任务中的效果，发现其预测准确性和信息量优于现有模型和分析师共识，且对投资者有显著经济价值。
We examine the efficacy of machine learning in a central task of fundamental analysis: forecasting corporate earnings. We find that machine learning models not only generate significantly more accurate and informative out-of-sample forecasts than the state-of-the-art models in the literature but also perform better compared to analysts’ consensus forecasts. This superior performance appears attributable to the ability of machine learning to uncover new information through identifying economically important predictors and capturing nonlinear relationships. The new information uncovered by machine learning models is of considerable economic value to investors. It has significant predictive power with respect to future stock returns, with stocks in the most favorable new information quintile outperforming those in the least favorable quintile by approximately 34 to 77 bps per month on a risk-adjusted basis.