Deep learning-based financial risk early warning model for listed companies: A multi-dimensional analysis approach

This study proposes a novel deep learning-based approach for financial risk early warning in listed companies through a hierarchical attention network that integrates multi-dimensional data sources. Traditional financial risk prediction models often struggle with complex non-linear relationships and fail to effectively combine diverse information types. We develop a comprehensive framework that simultaneously processes financial statements, market trading data, and textual information through specialized neural network components. The model employs a two-level attention mechanism that dynamically weights both individual features and information sources, enabling interpretable risk assessment. Using data from 2,876 Chinese A-share listed companies from 2015 to 2024, our empirical analysis demonstrates that the proposed model achieves superior predictive performance (AUC-ROC: 0.873) compared to traditional statistical approaches (0.742-0.768) and conventional machine learning methods (0.812-0.845). The model provides early warning signals approximately 4.2 months before actual distress events, significantly outperforming benchmark models (2.3-3.7 months). Notably, the model maintains robust performance during market stress periods (accuracy: 0.798) compared to traditional models (accuracy: 0.678). The attention mechanism reveals that the relative importance of different risk indicators varies systematically with market conditions, with financial ratios dominating during stable periods (weight: 0.435) and market signals becoming more crucial during crises (weight: 0.412). These findings contribute to both the theoretical understanding of financial risk dynamics and practical risk management applications, while demonstrating the effectiveness of interpretable deep learning approaches in financial analysis.