Research on the Application of ESG Index Quantitative Analysis Based on Machine Learning Algorithm in Financial Engineering

The lack of standardization and transparency of ESG (environment, society and governance) data limits the effectiveness of traditional quantitative analysis. In this paper, a quantitative analysis model of ESG indicators based on multi-source data fusion and machine learning algorithm is designed. The model integrates multi-dimensional indicator data of environment, society and governance. Through data cleaning, standardization and normalization, random forest (RF) is used as the main algorithm to predict ESG comprehensive score, and the Stacking integration method combined with support vector machine (SVM) and logistic regression (LR) is tried to further improve the prediction accuracy. The empirical analysis selects 500 large listed companies in the world as the research object, covering many industries. The results show that the integrated model has obvious advantages in forecasting the comprehensive score of ESG, the mean square error (MSE) is significantly reduced, and the determination coefficient is significantly improved, indicating that the model can more accurately reflect the actual changes of ESG score. In addition, by analyzing the model performance of different industries, it is found that the prediction error of science and technology and health care industries is small, while the prediction error of energy and manufacturing industries is relatively high. The research in this paper not only provides new ideas and methods for the quantitative analysis of ESG indicators, but also verifies the potential of machine learning algorithm in improving ESG evaluation accuracy and prediction ability through empirical analysis.