Estimating profitability decomposition frameworks via machine learning: Implications for earnings forecasting and financial statement analysis

• 成果类型: Article
• 署名作者: Binz, Oliver; Schipper, Katherine; Standridge, Kevin R.
• 署名单位: European School of Management & Technology; Duke University; Utah System of Higher Education; University of Utah
• 刊物名称: JOURNAL OF ACCOUNTING & ECONOMICS
• ISSN/ISSBN: 0165-4101; 1879-1980
• DOI: 10.1016/j.jacceco.2025.101805
• 发表日期: 2025
• 页码: 101805
• 关键词: financial statement analysis; Machine Learning; Earnings forecasting; IMPLIED COST

摘要

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.