With growing global financial market interconnectivity and cross-border risk transmission, precise measurement and intelligent warning of systemic risks in stock markets are vital for capital market stability. Traditional risk monitoring frameworks often fail to capture nonlinear dynamic correlations and extreme volatility. This study introduces a cross-market risk analysis system that integrates dynamic factor models with deep learning to identify risk transmission patterns and develop advanced warning tools.The Time Varying Generalized Dynamic Factor Model (tvGDFM) is used to assess how domestic financial shocks affect long-term inter-industry correlations over time. The analysis identifies specific industries, like agriculture and forestry, as risk contagion hubs during crises, with market shocks showing asymmetric transmission.On an international scale, expanding the two-stage generalized dynamic factor model and using event study methods reveals that capital market openness is a more significant driver of cross-border risk transmission than transaction time differences. The study also underscores the increased influence of common components in international market volatility during crises and the amplified risks from investors' home country bias and cross-border risk premiums.For intelligent early warning, a univariate model combining wavelet transform and a multi-time delayed recurrent neural network (WT-FCD-MLGRU) is developed. This model reduces prediction errors in stock indices by 15%-25% compared to traditional methods and enhances the detection of risk signals during extreme volatility by over 30%. A multi-dimensional deep learning architecture, incorporating technical, fundamental, and sentiment indicators, achieves a low false alarm rate of 0.39%, though it requires further optimization due to complex data.These findings provide regulators with a quantitative basis to improve industry monitoring, align capital market opening with risk management, and offer financial institutions tools to optimize cross-border investment risk management. This supports the creation of a cross-market risk prevention system utilizing diverse data sources, aiding global financial governance.

Qingyu Wang. Research on Intelligent Early Warning of Systemic Financial Risks in the Stock Market Based on Big Data and Multi Model Fusion. Socio-Economic Statistics Research (2025), Vol. 6, Issue 1: 74-84. https://doi.org/10.38007/SESR.2025.060108.

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