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JCSIS

Attention-Based Bidirectional LSTM Network for Early Detection of Financial Market Crashes and Systemic Risk Events.

Hakan Khan
Volume 5

Abstract

Financial market crashes and systemic risk events represent highly disruptive phenomena characterized by abrupt price collapses, volatility explosions, liquidity shortages, contagion effects, investor panic, and strong interdependence across financial institutions and asset classes. Early detection of such events is essential for risk management, portfolio protection, regulatory supervision, and the design of timely intervention strategies; however, conventional econometric models and traditional machine learning methods often struggle to capture nonlinear temporal dependencies, cross-market spillovers, and hidden warning signals preceding periods of financial instability. This paper proposes an attention-based Bidirectional Long Short-Term Memory network for the early detection of financial market crashes and systemic risk events. The proposed framework combines the sequential learning capability of BiLSTM networks with an attention mechanism that dynamically assigns higher importance to the most informative time steps and market indicators associated with emerging instability. Historical financial data from multiple markets, including equity indices, bond yields, foreign exchange rates, commodities, volatility indices, trading volume, credit spreads, and macro-financial indicators, were used to construct a comprehensive multivariate time-series dataset. A robust preprocessing pipeline was applied, including missing value imputation, normalization, return transformation, volatility estimation, rolling-window segmentation, feature lagging, and chronological train-validation-test splitting to prevent look-ahead bias. The model was trained to identify early-warning patterns preceding crash periods and systemic risk episodes by learning from both forward and backward temporal dependencies in financial sequences. To address the rarity of crisis events, class-weighted loss functions, focal loss, and oversampling strategies were incorporated during training. Experimental results demonstrate that the proposed attention-based BiLSTM model achieves superior predictive performance compared with baseline models including logistic regression, Random Forest, Support Vector Machine, XGBoost, standard LSTM, GRU, and conventional BiLSTM architectures. The proposed framework records higher accuracy, sensitivity, F1-score, AUC-ROC, and precision-recall performance, while reducing false negatives in crash-event detection. Furthermore, the attention mechanism enhances interpretability by identifying critical pre-crash indicators such as rising volatility, declining liquidity, widening credit spreads, abnormal trading volume, sharp correlation increases, and persistent negative return patterns. The findings indicate that the proposed model provides an adaptive, interpretable, and data-driven early-warning system for detecting financial market crashes and systemic risk events, offering practical value for investors, financial institutions, regulators, and automated risk-monitoring platforms operating in complex and highly interconnected global financial markets.

Keywords

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