MULTIVARIATE MODELING OF THE FINANCIAL STABILITY OF UKRAINIAN BANKS WITH FACTOR AND CLUSTER ANALYSIS METHODS
Abstract
The article presents a comprehensive study of the Ukrainian banking system using multivariate statistical analysis methods. To identify structural features, hidden patterns, weaknesses, and challenges faced by the banking system of Ukraine under wartime conditions, as well as to develop strategic adaptive solutions for banks, a clustering of banks was carried out based on key financial indicators selected through the principal component method. The research methodology is based on applying principal component analysis (PCA) to extract the main quantitative characteristics of banks’ financial stability, which were subsequently used for hierarchical clustering. The initial sample includes twelve financial indicators for 64 Ukrainian banks. At the first stage, the adequacy of the sample was confirmed using the KMO criterion (0.728) and the presence of significant relationships between variables was verified using Bartlett’s test of sphericity (p<0.01). Based on the Kaiser criterion, two key factors of financial stability were selected for further analysis: net interest income/expenses (correlation with the first component 0.99) and total assets (correlation with the second component 0.99), which together explain 90.43% of the variance. These indicators were then used for hierarchical clustering of Ukrainian banks using Ward’s method, nearest neighbor, and furthest neighbor approaches. The clustering results identified three clusters. The first cluster includes only PrivatBank as the market leader, explained by its scale and strategic significance in the Ukrainian banking system. The second cluster consists of systemically important banks (both state-owned and foreign-capital banks), while the third cluster comprises banks with the least influence on the country’s financial system. The clustering confirmed a clear differentiation of Ukraine’s banking system by the level of financial stability and its impact on the overall stability of the country. Thus, the application of PCA reduced the dimensionality of the data, and the use of clustering methods made it possible to identify systemically important banks that play a crucial role in maintaining the country’s financial stability.
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