V. D’Amato, R. L. D’Ecclesia, S. Levantesi
Corporate social responsibility (CSR) has a potential impact on firms performance, for instance enhancing firm reputation, increasing innovation capabilities, customer loyalty and customer satisfaction could help improve financial performance. However, the literature provides only limited evidence of the relationship between non-financial indicators, such as the ESG score, and the firm’s profitability, which is often measured by the earnings before interest and taxes (EBIT). We investigate this issue by analyzing a sample of about 400 companies constituting the EuroStoxx-600 index, from 2011 to 2020, using different machine learning models. The novelty of our contribution lies in assessing whether the ESG score has a significant influence on the firms’ profitability. Specifically, we deepen the relationship between ESG score and EBIT through machine learning interpretability toolboxes such as partial dependence plots and individual conditional expectation, which help to visualize the functional relationship between the predicted response and one or more features, and the Shapley value allowing to examine the contribution of the feature to the prediction. Our findings show that the model can reach high levels of accuracy in detecting EBIT and that the ESG score is a promising predictor, compared to other traditional accounting variables.
Parole Chiave: 
ESG investments, Firm’s performance, Machine Learning, Interpretability tools.
Tipo di pubblicazione: 
Rapporto Tecnico
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