Market state transitions and crash early warning in the Chinese stock market

Understanding systemic risk in financial markets requires tools that capture both structural complexity and dynamic evolution. This study adopts a complex systems perspective to analyze the Chinese stock market through correlation-based state classification. Using multidimensional scaling and K-means clustering on rolling stock return correlations, we identify five distinct market states that reflect varying degrees of systemic co-movement and exhibit strong temporal persistence and local transition patterns. We find that transitions among these states encode meaningful information about market structural shifts and are closely linked to the emergence of crash conditions. Building on this insight, we construct an early warning model using decision trees trained on temporal features derived from market state transitions—including medium-term state distributions, directional change ratios, and short-term evolutionary paths. The model achieves high recall and precision across configurations, and supports real-time adaptability via projection-based state labeling. These results highlight the value of market state dynamics and their transitions as a basis for systemic risk monitoring and crash anticipation in complex financial systems.