Machine Learning and Parameter Optimization for Banking Stability Prediction and Determinants Identification in ASEAN
This study leverages machine learning and advanced variable selection techniques to enhance the prediction of the Bank Financial Stability Index (Z-score) in emerging ASEAN markets. Utilizing a comprehensive secondary dataset comprising macroeconomic and bank-specific indicators from 61 commercial b...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Ital Publication
2025-06-01
|
| Series: | Emerging Science Journal |
| Subjects: | |
| Online Access: | https://ijournalse.org/index.php/ESJ/article/view/2940 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849420416384237568 |
|---|---|
| author | Pham Thuy Tu Dao Le Kieu Oanh Do Doan Trang |
| author_facet | Pham Thuy Tu Dao Le Kieu Oanh Do Doan Trang |
| author_sort | Pham Thuy Tu |
| collection | DOAJ |
| description | This study leverages machine learning and advanced variable selection techniques to enhance the prediction of the Bank Financial Stability Index (Z-score) in emerging ASEAN markets. Utilizing a comprehensive secondary dataset comprising macroeconomic and bank-specific indicators from 61 commercial banks across Indonesia, Malaysia, the Philippines, Singapore, Thailand, and Vietnam (2010–2023), we systematically evaluate the predictive power of multiple machine learning models. A rigorous cross-validation framework is employed to optimize forecasting accuracy, integrating Linear Regression, Random Forest, K-Neighbors, Decision Tree, Gradient Boosting, AdaBoost, Support Vector Regression, and XGBoost with Lasso, Ridge, and Elastic Net regularization. Empirical results reveal that key drivers of financial stability include equity capital, financial leverage, return on equity, GDP growth, inflation, technological advancements, and systemic shocks like the COVID-19 pandemic. Notably, the Ridge-optimized XGBRegressor model achieves the highest predictive accuracy (~89%), demonstrating the efficacy of hybrid machine learning approaches in financial stability forecasting. These findings offer crucial insights for policymakers and regulators, facilitating data-driven strategies to strengthen banking resilience and mitigate systemic risks in volatile economic environments.
Jel Classifier: C45, C52, C55, G21, G32. |
| format | Article |
| id | doaj-art-1e4cf51fc1194889a00ca2138dd215fb |
| institution | Kabale University |
| issn | 2610-9182 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Ital Publication |
| record_format | Article |
| series | Emerging Science Journal |
| spelling | doaj-art-1e4cf51fc1194889a00ca2138dd215fb2025-08-20T03:31:45ZengItal PublicationEmerging Science Journal2610-91822025-06-01931189120810.28991/ESJ-2025-09-03-042667Machine Learning and Parameter Optimization for Banking Stability Prediction and Determinants Identification in ASEANPham Thuy Tu0Dao Le Kieu Oanh1Do Doan Trang2Ho Chi Minh Universiy of Banking, 56 Hoang Dieu 2, Thu Duc, Ho Chi Minh CityHo Chi Minh Universiy of Banking, 56 Hoang Dieu 2, Thu Duc, Ho Chi Minh CityBinh Duong Universiy, 504 Binh Duong Boulevard, Thiep Thanh Ward, Thu Dau Mot City, Binh Duong ProvinceThis study leverages machine learning and advanced variable selection techniques to enhance the prediction of the Bank Financial Stability Index (Z-score) in emerging ASEAN markets. Utilizing a comprehensive secondary dataset comprising macroeconomic and bank-specific indicators from 61 commercial banks across Indonesia, Malaysia, the Philippines, Singapore, Thailand, and Vietnam (2010–2023), we systematically evaluate the predictive power of multiple machine learning models. A rigorous cross-validation framework is employed to optimize forecasting accuracy, integrating Linear Regression, Random Forest, K-Neighbors, Decision Tree, Gradient Boosting, AdaBoost, Support Vector Regression, and XGBoost with Lasso, Ridge, and Elastic Net regularization. Empirical results reveal that key drivers of financial stability include equity capital, financial leverage, return on equity, GDP growth, inflation, technological advancements, and systemic shocks like the COVID-19 pandemic. Notably, the Ridge-optimized XGBRegressor model achieves the highest predictive accuracy (~89%), demonstrating the efficacy of hybrid machine learning approaches in financial stability forecasting. These findings offer crucial insights for policymakers and regulators, facilitating data-driven strategies to strengthen banking resilience and mitigate systemic risks in volatile economic environments. Jel Classifier: C45, C52, C55, G21, G32.https://ijournalse.org/index.php/ESJ/article/view/2940aseanbankfinancial stabilityparameter optimizationmachine learningpredicting |
| spellingShingle | Pham Thuy Tu Dao Le Kieu Oanh Do Doan Trang Machine Learning and Parameter Optimization for Banking Stability Prediction and Determinants Identification in ASEAN Emerging Science Journal asean bank financial stability parameter optimization machine learning predicting |
| title | Machine Learning and Parameter Optimization for Banking Stability Prediction and Determinants Identification in ASEAN |
| title_full | Machine Learning and Parameter Optimization for Banking Stability Prediction and Determinants Identification in ASEAN |
| title_fullStr | Machine Learning and Parameter Optimization for Banking Stability Prediction and Determinants Identification in ASEAN |
| title_full_unstemmed | Machine Learning and Parameter Optimization for Banking Stability Prediction and Determinants Identification in ASEAN |
| title_short | Machine Learning and Parameter Optimization for Banking Stability Prediction and Determinants Identification in ASEAN |
| title_sort | machine learning and parameter optimization for banking stability prediction and determinants identification in asean |
| topic | asean bank financial stability parameter optimization machine learning predicting |
| url | https://ijournalse.org/index.php/ESJ/article/view/2940 |
| work_keys_str_mv | AT phamthuytu machinelearningandparameteroptimizationforbankingstabilitypredictionanddeterminantsidentificationinasean AT daolekieuoanh machinelearningandparameteroptimizationforbankingstabilitypredictionanddeterminantsidentificationinasean AT dodoantrang machinelearningandparameteroptimizationforbankingstabilitypredictionanddeterminantsidentificationinasean |