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

Abstract

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