Economic Analysis: Theory and Practice

Statistical analysis of bank activities using the advanced clustering

Vol. 17, Iss. 7, JULY 2018

Received: 7 May 2018

Received in revised form: 18 May 2018

Accepted: 29 May 2018

Available online: 27 July 2018


JEL Classification: С1, С6, G2

Pages: 1352–1366

Dotsenko O.S. Sevastopol State University, Sevastopol, Russian Federation

ORCID id: not available

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


  1. Petrov A.Yu., Petrova V.I. Kompleksnyi analiz finansovoi deyatel'nosti banka [Comprehensive analysis of bank's financial activities]. Moscow, Finansy i statistika Publ., 2007, 560 p.
  2. Dolan E.G., Campbell C.D., Campbell R.G. Den'gi, bankovskoe delo i denezhno-kreditnaya politika [Money, Banking and Monetary Policy]. Moscow, Profiko Publ., 1993, 446 p.
  3. Bor M.Z., Pyatenko V.V. Menedzhment bankov: organizatsiya, strategiya, planirovanie [Management of banks: Organization, strategy, planning]. Moscow, IKTs DIS Publ., 1997, 288 p.
  4. Golovach N.A. Методологія та організація статистичної інформації в аналізі банківської діяльності. In: Прикладна статистика: проблеми теорії та практики. Kiїv, Інформаційно-аналітичне агентство, 2010, 368 p.
  5. Rose P.S. Commercial Bank Management. Boston, McGraw-Hill/Irwin, 2002, 803 p.
  6. Ivanov V.V. Nadezhnost' vashego banka [The reliability of your bank]. Moscow, FBK-press Publ., 1997, 176 p.
  7. Sinkey J.F. Finansovyi menedzhment v kommercheskom banke i v industrii finansovykh uslug [Commerical Bank Financial Management in the Financial Services Industry]. Moscow, Al'pina Biznes Buks, Al'pina Pablisher Publ., 2007, 1024 p.
  8. Porter R.S. Introduction to Banking Regulation, Supervision and Bank Analysis. EDI, World Bank, 1993, 104 p.
  9. Ershov M.V., Zubov V.M. [Efficiency of the banking system: Challenging issues]. Den'gi i kredit = Money and Credit, 2005, no. 10, pp. 3–9. (In Russ.)
  10. Klyuchnikov M.V. [Analysis of indicators that characterize the financial activities of commercial banks]. Finansy i kredit = Finance and Credit, 2003, no. 20, pp. 40–45. URL: (In Russ.)
  11. Sablina E.A. [Statistical assessment of the Russian banking system condition after the crisis]. Voprosy Statistiki, 2011, no. 7, pp. 68–76. (In Russ.)
  12. Zamkovoi S.V. [Modeling the dynamics of the banking system and financial markets]. Bankovskoe delo = Banking, 2002, no. 7, pp. 9–12. (In Russ.)
  13. Demirguc-Kunt A., Detragiache E. The Determinants of Banking Crises in Developing and Developed Countries. IMF Staff Papers, 1998, vol. 45, no. 1, pp. 81–109.
  14. Kaminsky G., Reinhart C. The Twin Crises: The Causes of Banking and Balance-of-Payments Problems. The American Economic Review, 1999, vol. 89, iss. 3, pp. 473–500. URL:
  15. Chincarini L., Asherie N. An analytical model for the formation of economic clusters. Regional Science and Urban Economics, 2008, vol. 38, iss. 3, pp. 252–270. URL:
  16. Anderson T. Vvedenie v mnogomernyi statisticheskii analiz [An Introduction to Multivariate Statistical Analysis]. Moscow, Fizmatgiz Publ., 1963, 500 p.
  17. Kendall M., Stuart A. Mnogomernyi statisticheskii analiz i vremennye ryady [The Advanced Theory of Statistics: Design and Analysis, and Time-Series]. Moscow, Nauka Publ., 1976, 736 p.
  18. Maiba V.V. Кластерна структура банківської системи України. In: Прикладна статистика: проблеми теорії та практики. Київ, Інформаційно-аналітичне агентство, 2008, pp. 463–474.
  19. Kolmogorov A.N. Osnovnye ponyatiya teorii veroyatnostei [Basic concepts of the probability theory]. Moscow, Nauka Publ., 1974, 119 p.
  20. Kharkevich A.A. Bor'ba s pomekhami [Interference control]. Moscow, Nauka Publ., 1965, 274 p.

View all articles of issue


ISSN 2311-8725 (Online)
ISSN 2073-039X (Print)

Journal current issue

Vol. 17, Iss. 7
July 2018