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ESG Factors and Firms’ Credit Risk

Author

Listed:
  • Bonacorsi, Laura
  • Cerasi, Vittoria
  • Galfrascoli, Paola
  • Manera, Matteo

Abstract

We study the relationship between the risk of default and Environmental, Social and Governance (ESG) factors using Supervised Machine Learning (SML) techniques on a cross-section of European listed companies. Our proxy for credit risk is the z-score originally proposed by Altman (1968). We consider an extensive number of ESG raw factors sourced from the rating provider MSCI as potential explanatory variables. In a first stage we show, using different SML methods such as LASSO and Random Forest, that a selection of ESG factors, in addition to the usual accounting ratios, helps explaining a firm’s probability of default. In a second stage, we measure the impact of the selected variables on the risk of default. Our approach provides a novel perspective to understand which environmental, social responsibility and governance characteristics may reinforce the credit score of individual companies.

Suggested Citation

  • Bonacorsi, Laura & Cerasi, Vittoria & Galfrascoli, Paola & Manera, Matteo, 2022. "ESG Factors and Firms’ Credit Risk," FEEM Working Papers 329521, Fondazione Eni Enrico Mattei (FEEM).
    • Handle: RePEc:ags:feemwp:329521
    DOI: 10.22004/ag.econ.329521
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    More about this item

    Keywords

    Financial Economics; Productivity Analysis; Research Methods/ Statistical Methods;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • D4 - Microeconomics - - Market Structure, Pricing, and Design
    • G3 - Financial Economics - - Corporate Finance and Governance

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