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Environmental Concerns as Predictors of Bankruptcy and Financial Distress: An Explainable Artificial Intelligence Modelling

Author

Listed:
  • Hoang Hiep Nguyen

    (Métis Lab EM Normandie - EM Normandie - École de Management de Normandie = EM Normandie Business School)

Abstract

This study delves into the integration of environmental variables within financial risk assessment. Through the analysis of data from 1,265 French firms from 2003 to 2020, we illustrate how environmental factors can enhance the predictive accuracy of some machine learning models for financial distress prediction. Employing SHAP (SHapley Additive exPlanations), we additionally offer insights into their influence on these bankruptcy prediction models. Notably, our research represents a pioneering endeavor in utilizing the quantity of pollution emissions (mass of substances) stemming from industrial installations, contributing to air, soil, and water pollution, as an innovative indicator of environmental performance. This novel approach serves to emphasize the significance of diverse variables in assessing a firm's environmental performance within bankruptcy and financial distress prediction.

Suggested Citation

  • Hoang Hiep Nguyen, 2024. "Environmental Concerns as Predictors of Bankruptcy and Financial Distress: An Explainable Artificial Intelligence Modelling," Post-Print hal-04820742, HAL.
    • Handle: RePEc:hal:journl:hal-04820742
    as

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