Estimating profitability decomposition frameworks via machine learning: Implications for earnings forecasting and financial statement analysis
研究发现，使用非线性方法估计盈利能力分解框架，能比随机游走和线性估计更准确地预测未来盈利能力，且分解越细、聚焦核心项目、使用三年历史数据效果越好，预测还能解释股价回报。
We find that nonlinear estimation of profitability decomposition frameworks yields more accurate out-of-sample profitability forecasts than forecasts from both a random walk and linear estimation. The improvements derive from nonlinear estimation and synergies between nonlinear estimation and profitability decomposition frameworks. We analyze three essential financial statement analysis design choices to provide insights for the practice of fundamental analysis and find robust evidence that higher levels of profitability decomposition, focusing on core items, and using up to three years of historical information improve forecast accuracy. We find that our forecasts predict returns and profitability changes before and after controlling for analyst forecasts and common asset pricing factors.