Arbeitspapier
ESG Factors and Firms' Credit Risk
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Working Paper ; No. 036.2022
- Klassifikation
-
Wirtschaft
- Thema
-
Credit risk
Z-scores
ESG factors
Machine learning
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Bonacorsi, Laura
Cerasi, Vittoria
Galfrascoli, Paola
Manera, Matteo
- Ereignis
-
Veröffentlichung
- (wer)
-
Fondazione Eni Enrico Mattei (FEEM)
- (wo)
-
Milano
- (wann)
-
2022
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Bonacorsi, Laura
- Cerasi, Vittoria
- Galfrascoli, Paola
- Manera, Matteo
- Fondazione Eni Enrico Mattei (FEEM)
Entstanden
- 2022
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