Exploring the Relationship Between ESG Dimensions and Corporate Financial Performance: Evidence from U.S. Companies

In recent years, the concept of Environmental, Social, and Governance (ESG) investing has gained significant global attention, as it increasingly shapes investment strategies and corporate decision-making. This study aims to analyze the impact of ESG practices on corporate financial performance, particularly focusing on Return on Assets (ROA). Using data from U.S. publicly listed companies in 2018, various models including linear regression models, Principal Component Analysis (PCA), and other machine learning models (Random Forest, XGBoost, CatBoost) are employed for comprehensive analysis. The findings show that in the traditional linear regression model, social dimension has a significant positive impact on ROA, while the environmental and governance dimensions do not show statistical significance. However, machine learning models suggest that there may be nonlinear relationships between ESG dimensions and financial performance, with environmental factors potentially playing a dominant role. This study demonstrates that machine learning techniques outperform traditional methods in capturing the complex relationship between ESG and financial performance, providing valuable insights for businesses to balance social responsibility and financial objectives. Future research could expand to different regions and industries to further explore the heterogeneous impacts of ESG practices and the moderating role of policies.