ESG Factors and Firms' Credit Risk - Laura Bonacorsi; Vittoria Cerasi; Paola Galfrascoli; Matteo Manera (Fondazione Eni Enrico Mattei (FEEM), 2022)
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: C5; D4; G3; 330; Credit risk; Z-scores; ESG factors; Machine learning