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

Oliver Binz; Katherine Schipper; Kevin Standridge

doi:10.1016/j.jacceco.2025.101805

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

Oliver Binz¹; Katherine Schipper²; Kevin Standridge³

¹ European School of Management and Technology · ² Duke University · ³ University of Utah

Journal of Accounting and Economics 2025 open access

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.

DOI: 10.1016/j.jacceco.2025.101805
Volume: 80 (2-3)
Pages: 101805
Language: en
Export: BibTeX
Sources: openalex crossref