Research on Systemic Financial Risk Early Warning Based on Integrated Classification Algorithm

This study centers on the pivotal issue of systemic financial risk forecasting by employing ensemble classification algorithms to construct a warning model. The research delves into the application of ensemble learning methods such as Bagging, Boosting, and Random Forests in risk prediction. By curating samples and constructing a comprehensive dataset, this paper establishes a robust system for measuring systemic financial risk. In the empirical analysis, the optimized ensemble classification algorithms were applied to risk warning experiments within the Chinese financial market. The results indicate that the model demonstrates significant advantages in identifying potential systemic risks, with superior accuracy and stability compared to traditional single classification methods. The study also identifies macroeconomic indicators, financial market volatility, and cross-market correlations as key factors influencing systemic risk. The innovation of this paper lies in integrating advanced machine learning techniques with financial risk management theory, offering new insights and tools for regulatory authorities in formulating risk prevention policies. The findings hold substantial theoretical value and practical significance for enhancing the resilience of the financial system and maintaining financial stability.