这篇论文告诉你，当数据标签很少时，用半监督异常融合比复杂神经网络更靠谱。AAMSF在沪深300上AUC达0.68，而且发现英文新闻反而干扰预警。
论文提出AAMSF（异常增强多信号融合）半监督框架，在CSI 300数据集（791个训练日仅约80个正样本）上，AAMSF实现测试AUC-ROC 0.680，优于最强无监督基线（0.630）和神经网络基线（0.588）。T-AAMSF时间扩展版本将PR-AUC提升至0.291。消融实验显示GDELT事件与国内财经新闻提供互补风险信号，而英文媒体持续降低性能。研究建议在标签稀缺的金融风险预警中，稳健异常几何与源可靠性比监督表示能力更重要。
Extreme Volatility Warning under Label Scarcity via Multi-Source Anomaly Fusion
Early warning of extreme market volatility is central to financial risk management, but actionable events are rare, nonstationary, and often triggered by exogenous information shocks. In our CSI~300 setting, only $\sim$80 positive samples are observed across 791 training days, making heavily supervised multi-source models unstable. We first analyze a 100K-parameter hierarchical text-signal fusion model (HTSF) and find that added parameterization hurts in this low-label regime. Motivated by this failure, we propose \textbf{AAMSF} (Anomaly-Augmented Multi-Signal Fusion), a semisupervised framework that combines Isolation Forest anomaly scores over market indicators, GDELT events, Chinese financial news, and English media with lightweight Ridge score fusion. We further introduce \textbf{T-AAMSF}, a temporal extension for multi-day anomaly accumulation. On CSI~300 (2018--2023), AAMSF achieves test AUC-ROC \textbf{0.680}, outperforming the strongest unsupervised baseline (0.630) and neural baseline (0.588), while T-AAMSF improves PR-AUC to 0.291. Ablations reveal strong source asymmetry: GDELT and domestic financial news provide complementary risk signals, whereas English media consistently reduces performance, and learned weighting is unreliable under validation noise. These results suggest an empirical design principle for label-scarce financial risk warning: robust anomaly geometry and source reliability can matter more than supervised representation capacity.