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

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  • Binz, Oliver
  • Schipper, Katherine
  • Standridge, Kevin R.

Abstract

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.

Suggested Citation

  • Binz, Oliver & Schipper, Katherine & Standridge, Kevin R., 2025. "Estimating profitability decomposition frameworks via machine learning: Implications for earnings forecasting and financial statement analysis," Journal of Accounting and Economics, Elsevier, vol. 80(2).
    • Handle: RePEc:eee:jaecon:v:80:y:2025:i:2:s0165410125000412
    DOI: 10.1016/j.jacceco.2025.101805
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    References listed on IDEAS

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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting

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