This thesis investigates the relationship between corporate financial performance and Environmental, Social, and Governance (ESG) performance. The study examines whether financial indicators, including Return on Assets (ROA), Return on Equity (ROE), debt, and total assets, can explain variations in companies’ ESG scores. Using a panel dataset of 30 companies over a 15-year period, the analysis combines traditional econometric methods, including multiple and fixed-effects regression models, with machine-learning techniques such as Random Forest and XGBoost. The results indicate a strong positive relationship between financial profitability, particularly ROA and ROE, and ESG performance. The study further compares the predictive performance and variable importance across different modelling approaches. Overall, the findings provide evidence that financial performance can be an important determinant of corporate ESG performance and demonstrate the value of combining econometric and machine-learning methods in ESG research.
Questa tesi analizza la relazione tra performance finanziaria e performance Environmental, Social and Governance (ESG) delle imprese. Lo studio esamina in che misura indicatori finanziari quali Return on Assets (ROA), Return on Equity (ROE), indebitamento e attività totali possano spiegare le variazioni nei punteggi ESG delle aziende. L’analisi utilizza un dataset panel composto da 30 imprese osservate nell’arco di 15 anni e combina metodi econometrici tradizionali, tra cui regressioni multiple e modelli a effetti fissi, con tecniche di machine learning quali Random Forest e XGBoost. I risultati evidenziano una relazione positiva e significativa tra la redditività finanziaria, in particolare ROA e ROE, e la performance ESG. Lo studio confronta inoltre la capacità predittiva e l’importanza delle variabili nei diversi approcci di modellizzazione. Nel complesso, i risultati suggeriscono che la performance finanziaria può rappresentare un importante determinante della performance ESG e mostrano l’utilità di integrare metodi econometrici e di machine learning nell’analisi ESG.
Relazione tra ESG e performance finanziaria delle imprese
GURPREET KAUR, GURPREET KAUR
2025/2026
Abstract
This thesis investigates the relationship between corporate financial performance and Environmental, Social, and Governance (ESG) performance. The study examines whether financial indicators, including Return on Assets (ROA), Return on Equity (ROE), debt, and total assets, can explain variations in companies’ ESG scores. Using a panel dataset of 30 companies over a 15-year period, the analysis combines traditional econometric methods, including multiple and fixed-effects regression models, with machine-learning techniques such as Random Forest and XGBoost. The results indicate a strong positive relationship between financial profitability, particularly ROA and ROE, and ESG performance. The study further compares the predictive performance and variable importance across different modelling approaches. Overall, the findings provide evidence that financial performance can be an important determinant of corporate ESG performance and demonstrate the value of combining econometric and machine-learning methods in ESG research.| File | Dimensione | Formato | |
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THESIS Final work (1).pdf
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Descrizione: This thesis investigates the relationship between corporate financial performance and Environmental, Social, and Governance (ESG) performance.
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https://hdl.handle.net/20.500.14239/36907