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

Hoang Hiep Nguyen ()
Additional contact information
Hoang Hiep Nguyen: Métis Lab EM Normandie - EM Normandie - École de Management de Normandie = EM Normandie Business School

Post-Print from HAL

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.

Keywords: Machine learning; Shapley Additive Explanations; Environmental variables; Bankruptcy prediction; Financial distress (search for similar items in EconPapers)
Date: 2024-04-27
References: Add references at CitEc
Citations:

Published in 40th International Conference of the French Finance Association (AFFI), Apr 2024, Lille, France

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04820742

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().