Advancing National Economic Resilience: A Machine Learning substructure for Systemic Early Warning Systems for Systemic Financial Risk (EWS)

Abstract

Systemic financial risk represents a major challenge to national economic stability, as it can initiate chain reactions of failures among interconnected financial institutions and markets. Conventional econometric techniques, such as Vector Autoregression and logistic regression, often struggle to capture the complex, non-linear relationships and high-dimensional interactions present in modern financial systems, resulting in delayed or inadequate early warning signals. This study introduces and validates a novel machine learning framework aimed at forecasting systemic risk indicators with greater accuracy and timeliness. The proposed approach combines ensemble learning techniques, including Gradient Boosting Machines and Random Forest, with deep learning models such as Long Short-Term Memory networks. It analyses a comprehensive panel dataset of over 200 financial and macroeconomic variables from major global economies spanning 2000 to 2023, forecasting systemic expected shortfall, conditional value-at-risk, and composite financial instability indices.

Empirical findings demonstrate that the framework substantially outperforms traditional models, achieving a 23% improvement in out-of-sample prediction accuracy and reducing false negative rates for crisis events by approximately 40%. Notably, the model identifies early warning signals 6–12 months before historical crises, including the 2008 financial crisis and COVID-19 market instability. Ablation testing confirms that capturing non-linear relationships and temporal dependencies is crucial for superior performance. The results carry significant policy implications, providing macroprudential regulators with a powerful, data-driven tool for proactive risk monitoring and timely interventions. SHAP value analysis enhances interpretability by revealing key systemic risk drivers, while stable performance across economic environments suggests potential as a standardized tool for global financial stability monitoring. This research bridges the gap between theoretical risk measurement and practical policy implementation, contributing to stronger economic resilience against systemic shocks.

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