Abstract
ESG ratings, as a metric for assessing corporate social responsibility, have garnered escalating interest from both domestic and international investors within China. However, critical indicators for augmenting ESG scores remain underexplored. Utilizing data from 2017 to 2020 on CSI 300 and CSI 500 constituent stocks, this study employs advanced machine learning algorithms, including XGBoost (XGB), LightGBM (LGB), and Random Forest (RF), to conduct a quantitative analysis of corporate feature values and their correlation with institutional rating outcomes. The findings indicate a predominant influence of corporate governance indicators, particularly in the realm of information disclosure, followed by social responsibility and environmental metrics. The paper further delineates specific, actionable short-term and long-term strategies that corporations can adopt across environmental, social, and governance dimensions, tailored to the nuances of various sub-indicators.
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