Company Revenue Prediction Based on ESG Risk Rating for Sustainable Finance using XGBoost Algorithm

Sustainable finance is a process that considers Environmental, Social, and Governance (ESG) when making investment decisions in the financial sector. This approach leads to more long-term investments in sustainable economic activities and projects. In today's interconnected global economy, stakeholders recognize that a company's ESG responsibilities are integral to its long-term performance and sustainability. Therefore, this research proposed a method for predicting company revenues based on the ESG Risk Rating Score and other factors such as employees, profits, assets, and market value. The research comprises four stages. The first stage involves data preparation, followed by training the model using Extreme Gradient Boosting (XGBoost) algorithm. The third stage is hyperparameter tuning of the model using the GridSearchCV library, while the fourth stage is performance evaluation using R-squared (R²), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The experimental results showed that the XGBoost models produced R² values 0.8537.