We explore the relationship between credit risk and Environmental, Social, and Governance (ESG) dimensions 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). As potential explanatory variables, we consider an extensive number of raw ESG factors sourced from the rating provider MSCI. In the first stage, we demonstrate, 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 the second stage, we measure the impact of the selected variables on the risk of default. Our approach provides a novel perspective to understand which ESG factors may be associated with the credit score of individual companies.

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Questo articolo scientifico è stato pubblicato in precedenza nella serie FEEM Working Papers come ‘Nota di Lavoro’ 032.2022.