Analysis of Evolving Hazard Overflows and Construction of an Alert System in the Chinese Finance Industry Using Statistical Learning Methods

With the global economic situation still uncertain and various businesses interconnected within finance system, financial hazards exhibit characteristics such as rapid propagation wide scope. Therefore, it is of great significance to analyze evolving changes patterns hazard overflow in industry construct a alert system. We adopt time-varying parameter vector auto-regressive model examine degree alerts from an perspective measurement indicators. To effectively prevent consider non-linear causal relationship between macroeconomic variables, we utilize long/short-term memory network model, which can capture temporal features, Furthermore, explore whether inclusion online sentiment indicator enhance accuracy alerts, aiming provide policy recommendations on strengthening market stability establishing mechanism under macro-prudential supervision.