ESG Factors and Firms’ Credit Risk

Laura Bonacorsi, Vittoria Cerasi, Paola Galfrascoli and Matteo Manera ()

No 329521, FEEM Working Papers from Fondazione Eni Enrico Mattei (FEEM)

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.

Keywords: Financial Economics; Productivity Analysis; Research Methods/ Statistical Methods (search for similar items in EconPapers)
Pages: 52
Date: 2022-11-29
New Economics Papers: this item is included in nep-ban, nep-big, nep-cfn, nep-env and nep-rmg
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https://ageconsearch.umn.edu/record/329521/files/NDL2022-036.pdf (application/pdf)

Related works:
Working Paper: ESG Factors and Firms’ Credit Risk (2022)
Working Paper: ESG Factors and Firms' Credit Risk (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:ags:feemwp:329521

DOI: 10.22004/ag.econ.329521

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