ESG Factors and Firms’ Credit Risk
52 Pages Posted: 30 Nov 2022
There are 2 versions of this paper
ESG Factors and Firms’ Credit Risk
ESG Factors and Firms’ Credit Risk
Date Written: November 29, 2022
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: Credit risk, Z-scores, ESG factors, Machine learning
JEL Classification: C5, D4, G3
Suggested Citation: Suggested Citation