Can Machine Learning Explain Alpha Generated by ESG Factors?

Abstract This research explores the use of machine learning to predict alpha in constructing portfolios, leveraging a broad array of environmental, social, and governance (ESG) factors within the S&P 500 index. Existing literature bases analyses on synthetic indicators, this work proposes an analytical deep dive based on a dataset containing the sub-indicators that give rise to the aforementioned synthetic indices. Since such dimensionality of variables requires specific processing, we deemed it necessary to use a machine learning algorithm, allowing us to study, with strong specificity, two types of relationships: the interaction between individual ESG variables and their effect on corporate performance.The results clearly show that ESG factors have a significant relationship with company performance. These findings emphasise the importance of integrating ESG indicators into quantitative investment strategies using Machine Learning methodologies.