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<p>Environmental, Social, and Governance (ESG) factors have become increasingly relevant in financial markets, influencing investment strategies and risk assessments. This article explores the role of raw ESG metrics in predicting the direction of future stock returns, framing return forecasting as a classification problem. We analyse MSCI ACWI index components from 2016 to 2022, focusing on the manufacturing, information, and financial sectors in the USA and Europe. We propose an ESG-oriented data cleaning pipeline and evaluate various machine learning models, finding that XGBoost outperforms other approaches. To assess the predictive power of ESG metrics, we conducted an ablation study, comparing their contribution to benchmark financial variables and past returns. Our results show that ESG and financial variables independently improve classification performance comparably, suggesting a complementary role in return forecasting. Through a SHAP-based feature importance analysis, we examine ESG contributions at the sector-region level, revealing that Environmental and Governance factors are generally the most influential in predictive performance. Our findings suggest that raw ESG metrics contain meaningful predictive value that should not be overlooked.</p>

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Returns under the lens: the importance of ESG factors

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月30日

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  • Gabriele Ginestroni,
  • Daniele Marazzina,
  • Nico Rosamilia

摘要

Environmental, Social, and Governance (ESG) factors have become increasingly relevant in financial markets, influencing investment strategies and risk assessments. This article explores the role of raw ESG metrics in predicting the direction of future stock returns, framing return forecasting as a classification problem. We analyse MSCI ACWI index components from 2016 to 2022, focusing on the manufacturing, information, and financial sectors in the USA and Europe. We propose an ESG-oriented data cleaning pipeline and evaluate various machine learning models, finding that XGBoost outperforms other approaches. To assess the predictive power of ESG metrics, we conducted an ablation study, comparing their contribution to benchmark financial variables and past returns. Our results show that ESG and financial variables independently improve classification performance comparably, suggesting a complementary role in return forecasting. Through a SHAP-based feature importance analysis, we examine ESG contributions at the sector-region level, revealing that Environmental and Governance factors are generally the most influential in predictive performance. Our findings suggest that raw ESG metrics contain meaningful predictive value that should not be overlooked.