荆楚理工学院学报2026，Vol.41Issue(2)：85-95,11.
基于GATv2-RF-SHAP模型的系统性金融风险预警研究
Research on Early Warning of Systemic Financial Risk Based on GATv2-RF-SHAP Model
摘要
Abstract
Effective early warning of systemic financial risk is crucial for maintaining financial security.This study integrates Random Forest algorithm,Factor Analysis,and an improved Graph Attention Network ﹙GATv2)to develop a more effective sys-temic financial risk early warning model.Based on seven dimensions—the macro economy,stocks,bonds,exchange rates,com-modities,interest rates,and financial institutions—16 indicators are selected to construct a systemic financial risk warning system for China.By combining GATv2 with factor analysis,an efficient Financial Stress Index is extracted,overcoming the path depen-dence of traditional factor models on linear assumptions.GATv2's multi-head dynamic attention mechanism uncovers nonlinear relationships between indicators,while the Random Forest algorithm determines the importance of each indicator.The SHAP model is used to visualize the feature contribution of different indicators to risk.The results show that the Random Forest algorithm per-forms well in risk prediction.In the risk warning indicator system,short-term and medium-to-long-term loan interest rates,bond market custody balance,and the loan-to-deposit ratio emerge as key risk drivers,with short-term loan interest rates negatively in-fluencing systemic financial risk.Based on these findings,recommendations for risk prevention and control are provided to support financial security decision-making.关键词
系统性金融风险/因子分析/图注意力网络/随机森林/SHAP模型Key words
systemic financial risk/factor analysis/graph attention network/random forest/SHAP model分类
管理科学引用本文复制引用
韩光辉,蔡金铭,曹自锦,杨帆..基于GATv2-RF-SHAP模型的系统性金融风险预警研究[J].荆楚理工学院学报,2026,41(2):85-95,11.基金项目
河北省高等学校人文社会科学研究项目﹙SY2022043) （）
