A Multimodal Deep Learning Architecture Fusing Real-Time High-Frequency Market Data, Global News Sentiment, and Macroeconomic Signals for Systemic Financial Risk Prediction

• Abbas Ahsun

  Texas University

  Author

Systemic financial risk prediction remains a critical challenge as traditional econometric models struggle with nonlinear interactions, regime shifts, and heterogeneous data sources that characterize modern financial systems. This study addresses the gap by developing and validating a multimodal deep learning architecture that fuses real-time high-frequency market data, global news sentiment, and macroeconomic signals for systemic risk prediction. The proposed architecture employs modality-specific encoders with cross-attention fusion mechanisms to capture dynamic interactions across data streams. Using a dataset spanning 2015–2024 comprising tick-level market data, FinBERT-processed news sentiment, and macroeconomic indicators, the model achieves 89.4% accuracy and 0.912 AUC-ROC in predicting systemic stress events with an average lead time of 8.3 trading days. Feature importance analysis reveals news sentiment accounts for 34.2% of predictive weight, followed by high-frequency volatility measures (28.7%) and macroeconomic signals (22.1%). These findings demonstrate that multimodal fusion significantly outperforms unimodal and traditional econometric baselines, providing a replicable framework for real-time systemic risk monitoring with practical implications for regulators and financial institutions.

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Copyright (c) 2026 Abbas Ahsun (Author)

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