RicciFlowRec: A Geometric Root Cause Recommender Using Ricci Curvature on Financial Graphs
This addresses the problem of risk-aware financial decision-making for investors, though it appears incremental as it builds on existing geometric methods in a new domain.
The paper tackles the problem of root cause attribution in financial decision support by proposing RicciFlowRec, a geometric recommendation framework that uses Ricci curvature and flow on dynamic financial graphs to quantify local stress and trace shock propagation. Preliminary results on S&P 500 data with FinBERT-based sentiment show improved robustness and interpretability under synthetic perturbations.
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.