To effectively prevent the outbreak of financial crises, constructing a resilient early-warning mechanism now constitutes a core mandate in macroprudential supervision. This paper employs literature review methods, combined with case analysis and the macroprudential regulatory framework, to explore optimization methods for early warning indicators of financial crises under new trends in financial regulation, and proposes innovative approaches. The paper investigates how to identify and quantify the three key drivers of financial crises: macroeconomic imbalances, market over-speculation, and financial institution risks; innovative approaches leveraging high-frequency financial datasets with ensemble learning algorithms to enhance the real-time accuracy of early warning indicators; the synergistic effects of cross-level regulatory information sharing, multi-stakeholder collaborative processes, and two-way feedback mechanisms; and demonstrates that constructing a multi-dimensional indicator system covering macro, market, and institutional levels can comprehensively reflect the accumulation and propagation of systemic risks. The utilization of big data feature engineering and machine learning models has significantly improved the prompt detection of warning signals, resulting in a marked decrease in false alarm rates. Information sharing and multi-party collaborative warning processes, combined with two-way feedback between market participants and regulators, have achieved closed-loop management and synergistic efficiency gains in the warning system.
Shixin Xu (Wed,) studied this question.