用几何曲率分析金融图谱，定位风险源头并推荐稳健投资
RicciFlowRec: A Geometric Root Cause Recommender Using Ricci Curvature on Financial Graphs
- 基于离散里奇曲率与流，量化金融节点间局部压力
- 在标普500数据上，合成扰动下鲁棒性提升，可解释性增强
- 适合风控、量化投资及金融决策支持场景
我们提出RicciFlowRec，一种基于几何曲率与流的金融图谱推荐框架，通过建模股票、宏观经济指标与新闻间的动态交互，利用离散里奇曲率量化局部压力，并通过里奇流追踪冲击传播。曲率梯度揭示因果子结构，用于构建结构风险感知的排序函数。在基于FinBERT情感分析的标普500数据上，初步结果表明该方法在合成扰动下具有更强鲁棒性与可解释性。本研究为基于曲率的风险归因与早期风险感知排序提供支持，未来计划拓展至组合优化与收益预测。据我们所知，RicciFlowRec是首个在金融决策支持中应用几何流推理的推荐系统。
原文摘要 · Abstract (English)
We propose RicciFlowRec, a geometric recommendation framework that performs root cause attribution via Ricci curvature and flow on dynamic financial graphs. By modelling evolving interactions among stocks, macroeconomic indicators, and news, we quantify local stress using discrete Ricci curvature and trace shock propagation via Ricci flow. Curvature gradients reveal causal substructures, informing a structural risk-aware ranking function. Preliminary results on S\&P~500 data with FinBERT-based sentiment show improved robustness and interpretability under synthetic perturbations. This ongoing work supports curvature-based attribution and early-stage risk-aware ranking, with plans for portfolio optimization and return forecasting. To our knowledge, RicciFlowRec is the first recommender to apply geometric flow-based reasoning in financial decision support.
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