The role of Environmental, Social, and Governance (ESG) in predicting bank financial distress

Роль экологических, социальных и управленческих факторов (ESG) в прогнозировании финансовых трудностей банков
Alberto Citterio, Timothy King
2022-10-12

Environmental, Social, and Governance (ESG)bank financial distressdefault predictionensemble methodsmachine learning
We analyze the predictive power of Environmental, Social, and Governance (ESG) indicators to forecast bank financial distress using a sample of 362 commercial banks headquartered in the US and EU-28 members states from 2012 to 2019. Our results demonstrate that ESG improves the predictive capability of our model to correctly identify distress. Notably, ESG strongly reduces the likelihood of misclassifying distressed/defaulted banks as healthy. Our model, which we estimate using six alternative approaches, including traditional statistical techniques, machine learning approaches, and ensemble methods, has implications for both practical implications by banking sector supervisors, as well as literature on default prediction.
1
ESG indicators improve the ability to predict financial distress among 362 commercial banks in the US and EU-28 from 2012–2019.
2
Including ESG substantially reduces the likelihood of misclassifying distressed or defaulted banks as financially healthy.
3
The findings support using ESG information in bank supervision and contribute to the literature on bank default prediction.
4
The predictive benefits of ESG are observed across six approaches, including statistical, machine-learning, and ensemble methods.

US and EU-28 commercial banks

The predictive power of Environmental, Social, and Governance (ESG) indicators for forecasting financial distress and reducing misclassification of distressed/defaulted banks as healthy

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Publication Date
2022-10-12
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Authors
Alberto Citterio
Timothy King
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