Advancing National Economic Resilience: A Machine Learning Framework for Systemic Financial Risk Forecasting
DOI:
https://doi.org/10.64882/ijrt.v13.i1.976Keywords:
Systemic Financial Risk, Economic Resilience, Machine Learning, Forecasting, Financial Stability, Macroprudential PolicyAbstract
Systemic financial risk stands for a critical threat to national economic resilience, capable of triggering cascading failures across interconnected financial institutions and markets. Traditional econometric forecasting models-including Vector Autoregression (VAR) and logistic regression-exhibit fundamental limitations in capturing the non-linear dynamics and high-dimensional interactions that characterize modern financial architectures, often resulting in delayed or missed early warning signals. This paper proposes and confirms a novel machine learning framework designed to forecast key systemic risk indicators with enhanced precision and timeliness.
Our method integrates ensemble methods (Gradient Boosting Machines and Random Forest) with deep learning architecture (Long Short-Term Memory networks) to analyze a comprehensive, high-dimensional panel dataset including over 200 financial and macroeconomic variables across major economies from 2000 to 2023. The framework is trained to predict systemic expected shortfall, conditional value-at-risk, and composite instability indices, employing advanced feature engineering and temporal cross-validation to ensure robust out-of-sample performance.
Empirical results show that the ML framework significantly outperforms traditional benchmarks, achieving a 23% improvement in out-of-sample forecasting accuracy and reducing false negative rates by 40% for crisis events. Critically, the model successfully names early warning signals 6–12 months ahead of historical episodes, including the 2008 fiscal crisis and recent pandemic-related market stress. Ablation studies confirm that capturing non-linear interactions and temporal dependencies drives this superior performance.
The policy implications are profound: this framework equips macroprudential regulators with a superior, data-driven tool for initiative-taking risk surveillance, enabling prompt implementation of countercyclical buffers and targeted interventions. By operationalizing innovative ML techniques, this research bridges the critical gap between theoretical risk measurement and practical policy application, ultimately strengthening national economic resilience against future systemic shocks. Furthermore, SHAP value analysis enhances model interpretability, providing regulators with transparent insights into key risk drivers. The framework also proves robust performance across diverse economic regimes, supporting its potential as a standardized tool for international financial stability surveillance.
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