Hoang Thi Thanh Hang

Type of the article: Research Article

Abstract
The study analyzes the role of the Funding Gap (FGAP) as a dynamic structural liquidity indicator that influences bank financial stability in emerging markets, particularly amid heightened post-COVID-19 financial volatility. It aims to forecast banking stability by integrating advanced econometric and machine-learning techniques using a balanced panel dataset of 63 commercial banks from six ASEAN countries over the period 2010–2023. The methodological framework combines Ridge regression for variable selection, Particle Swarm Optimization (PSO) for hyperparameter tuning, and SHapley Additive exPlanations (SHAP) for interpretability within a Gradient Boosting model. The PSO-optimized specification achieves an R2 of 92.2%, substantially outperforming traditional fixed-effects and random-effects regressions. Empirical results indicate that persistent negative FGAP values significantly reduce Z-scores, confirming that structural liquidity imbalances constitute a key transmission channel from funding stress to systemic fragility. The analysis further reveals the moderating role of macroeconomic shocks, particularly inflation and the COVID-19 pandemic, in amplifying liquidity-induced instability. The proposed framework functions as an operational early warning system that enhances forecasting accuracy, model interpretability, and regulatory transparency, while repositioning FGAP as a forward-looking liquidity metric and offering both theoretical and practical contributions to financial risk management and supervisory practices in emerging economies.