用多层图模型提前预警市场崩盘，捕捉系统性风险
Systemic Risk Radar: A Multi-Layer Graph Framework for Early Market Crash Warning
- 将金融市场建模为多层图，捕捉跨部门、跨行为的复杂关联
- 在三次重大危机中，图特征比传统模型更早识别系统脆弱性
- 适合关注金融风控、量化交易与宏观预警的研究者
金融危机源于各领域、市场与投资者行为之间结构漏洞的累积。预测这类系统性转变极具挑战，因其根植于市场参与者的动态交互，而非孤立的价格波动。本文提出系统性风险雷达（SRR），将金融市场建模为多层图，以检测系统性脆弱性的早期信号及崩盘状态转移。我们在网络泡沫破灭、全球金融危机和新冠疫情冲击三大危机中评估SRR。实验对比了快照GNN、简化时序GNN原型与标准基线（逻辑回归、随机森林）。结果表明，相比仅依赖特征的模型，结构化网络信息能提供更有价值的早期预警信号。该基于相关性的实例验证了图衍生特征在压力事件中可捕捉市场结构的实质性变化。研究建议未来扩展SRR至更多图层（如行业/因子暴露、情绪）及更强大的时序架构（如LSTM/GRU或Transformer编码器），以应对多样化的危机类型。
原文摘要 · Abstract (English)
Financial crises emerge when structural vulnerabilities accumulate across sectors, markets, and investor behavior. Predicting these systemic transitions is challenging because they arise from evolving interactions between market participants, not isolated price movements alone. We present Systemic Risk Radar (SRR), a framework that models financial markets as multi-layer graphs to detect early signs of systemic fragility and crash-regime transitions. We evaluate SRR across three major crises: the Dot-com crash, the Global Financial Crisis, and the COVID-19 shock. Our experiments compare snapshot GNNs, a simplified temporal GNN prototype, and standard baselines (logistic regression and Random Forest). Results show that structural network information provides useful early-warning signals compared to feature-based models alone. This correlation-based instantiation of SRR demonstrates that graph-derived features capture meaningful changes in market structure during stress events. The findings motivate extending SRR with additional graph layers (sector/factor exposure, sentiment) and more expressive temporal architectures (LSTM/GRU or Transformer encoders) to better handle diverse crisis types.
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