ESG Factors and Firms’ Credit Risk

We study the relationship between the risk of default and Environmental, Social, and Governance (ESG) factors using Machine Learning (ML) 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 the 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 explain 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 environmental, social responsibility, and governance characteristics may reinforce the credit score of individual companies