Subject The article addresses procedures for statistical and mathematical analysis of bank activities. Objectives The study aims to improve methods of multidimensional grouping of banks and forecasting their activities using mathematical models, i.e. clustering based on similarity between scalar banks – weighted Euclidean distance, and vector modification of cluster analysis based on similarity – the angle of slope between banks-vectors characterized by weighted indicators. Methods I apply statistical and mathematical techniques, like statistical observation, banks' grouping by individual indicators and a suite of metrics using the factor, cluster, discriminant and vector analysis, methods of analyzing time series and relationships between indicators. Results I developed generalized classifications of banks based on three attributes, namely, asset size, weighted Euclidean distance and the slope angle between weighted characteristics of banks. It helps avoid contradictions in grouping the objects and clarify principles of their work by clusters, considering the size of assets and dynamics in slope angles. The forecast of the particular bank's policy using the vector representation of its indicators enabled to assess the bank's stability over the analyzed period of time. The analysis techniques may be useful for bank CEOs, customers and counterparties when choosing a stable and reliable bank. Conclusions Under the presented methodology, the bank grouping on the basis of a set of indicators is more homogeneous and the principles of bank activities have better interpretations.
Keywords: bank, cluster analysis, weighted Euclidean distance, vector, slope angle
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