Predicting Enterprise Bankruptcy With HBA‐DGNN: An Innovative Approach by Hypergraph and Bidirectional Attention‐Based Dual GNNs

ABSTRACT Enterprise bankruptcy exerts effects on financially linked firms‘ operations. Such risks may propagate through shareholding relationships, potentially amplifying financial distress and threatening the stability of the broader system. Enterprise risks originate not only from internal factors but also from complex equity relationship networks. Consequently, there is a critical need for enterprise bankruptcy prediction models to support investment and operational risk management. We propose hypergraphs and bidirectional attention‐based dual graph neural networks (HBA‐DGNN) as an innovative approach for predicting enterprise bankruptcy. It consists of two main components. The first component, the hypergraph embeddings of categorical features (HECF) module, can effectively capture higher order relationships among enterprises. Simultaneously, the bidirectional attention‐based GNN (BAG) module quantifies the importance of equity relationships based on enterprise attributes and networks. We conduct an empirical study on the model of Evergrande Group, which faced a debt crisis and caused systemic risks in China's financial market. The HBA‐DGNN demonstrates superior predictive performance compared to baseline models, achieving an average improvement of over 20%. Additionally, attention coefficients in the BAG significantly correlate with enterprise bankruptcy, effectively identifying critical edges and nodes responsible for risk contagion. The HBA‐DGNN effectively predicts enterprise bankruptcy, supporting corporate operations, financial investment, and market supervision.