Estimating the BIS capital adequacy ratio for Korean banks using machine learning: Predicting by variable selection using random forest algorithms

• Published in: Risks (Basel) Vol. 9; no. 2; pp. 1 - 19
• Main Authors: Park, Jaewon, Shin, Minsoo, Heo, Wookjae
• Format: Journal Article
• Language: English
• Published: Basel MDPI 01.02.2021; MDPI AG
• Subjects: bank; Banking industry; Bayesian regulatory neural network; BIS capital adequacy ratio; capital adequacy; Capital costs; Central banks; Credit risk; Equity capital; International finance; Machine learning; random forest algorithms; Regulation of financial institutions; Regulatory reform; Supervision; Variables; South Korea
• ISSN: 2227-9091; 2227-9091
• DOI: 10.3390/risks9020032

The purpose of this study is to find the most important variables that represent the future projections of the Bank of International Settlements' (BIS) capital adequacy ratio, which is the index of financial soundness in a bank as a comprehensive and important measure of capital adequacy. This study analyzed the past 12 years of data from all domestic banks in South Korea. The research data include all financial information, such as key operating indicators, major business activities, and general information of the financial supervisory service of South Korea from 2008 to 2019. In this study, machine learning techniques, Random Forest Boruta algorithms, Random Forest Recursive Feature Elimination, and Bayesian Regularization Neural Networks (BRNN) were utilized. Among 1929 variables, this study found 38 most important variables for representing the BIS capital adequacy ratio. An additional comparison was executed to confirm the statistical validity of future prediction performance between BRNN and ordinary least squares (OLS) models. BRNN predicted the BIS capital adequacy ratio more robustly and accurately than the OLS models. We believe our findings would appeal to the readership of your journal such as the policymakers, managers and practitioners in the bank-related fields because this study highlights the key findings from the data-driven approaches using machine learning techniques.