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

Abstract

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.