Multi-Modal Financial Distress Prediction Through the Fusion of Price Dynamics, Macroeconomic Signals, and Corporate Disclosures
Keywords:
financial distress prediction, multi-modal fusion, price dynamics, macroeconomic signals, corporate disclosures, systemic risk, data leakage, benchmark design, artificial intelligence governanceAbstract
Financial distress prediction remains a central challenge in quantitative finance, risk management, and regulatory oversight. Traditional models relying on a single data modality, such as accounting ratios or market prices, suffer from limited predictive power, temporal instability, and an inability to capture the multi-faceted nature of economic crises. This paper proposes a multi-modal framework that fuses three distinct signal families: high-frequency price dynamics, macroeconomic indicators, and structured corporate disclosures. We argue that no single information channel is sufficient to anticipate the complex interplay of firm-specific vulnerabilities, systemic shocks, and informational frictions. The proposed architecture integrates temporal attention mechanisms and cross-modal fusion layers to learn hierarchical representations that reflect both micro-level financial health and macro-level stress propagation. We discuss the structural trade-offs inherent in model design, including the tension between predictive accuracy and interpretability, the risks of data leakage in benchmark construction, and the challenges of deploying such systems across heterogeneous regulatory environments. Special attention is given to the evaluation of market-stress early warning systems, where traditional backtesting often conflates statistical fit with economic credibility. Drawing on recent advances in leakage-safe benchmark design and attention-enhanced reinforcement learning, we outline a governance framework that promotes robustness, fairness, and sustainability. The paper concludes with policy implications for systemic risk monitoring and the responsible integration of multi-modal artificial intelligence into financial infrastructure. Our analysis underscores that the fusion of diverse signals, while technically demanding, offers the most promising path toward resilient distress prediction systems that can operate under extreme uncertainty.
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