ESG Factors and Firms' Credit Risk

Arbeitspapier

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.

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• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Paper ; No. 036.2022

Classification and Topics

• Classification: Wirtschaft
• Subject: Credit risk ; Z-scores ; ESG factors ; Machine learning

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Bonacorsi, Laura ; Cerasi, Vittoria ; Galfrascoli, Paola ; Manera, Matteo
• Event: Veröffentlichung
• (who): Fondazione Eni Enrico Mattei (FEEM)
• (where): Milano
• (when): 2022

Further information

• Handle: http://hdl.handle.net/10419/267501