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Clustering Austrian Banks’ Business Models and Peer Groups in the European Banking Sector

Author

Listed:
  • Robert Ferstl

    (Off-Site Banking Analysis and Strategy Division)

  • David Seres

    (Off-Site Banking Analysis and Strategy Division)

Abstract

As the European banking sector is becoming increasingly intertwined, the degree of interdependence is also rising. Consequently, it is key to conduct comparisons for a timely identification of emerging patterns of this development. Furthermore, the product range of banks has expanded so that heterogeneity across the banking sector has also been growing rapidly. This rising heterogeneity makes it increasingly impractical to carry out comparisons on an aggregate level. A more efficient approach is identifying one or ore ”common denominators” of similar banks and establishing groups of banks which share this (these) common denominator(s). In this paper, we consider the business models of banks as one such common denominator, which can be described by a set of variables. These variables span a high-dimensional space where each bank represents a point, which can be measured by a statistical distance. Points close to each other may constitute a group, while points distant from these points will not belong to that group. Therefore, the objective of this study is, on the one hand, to define an efficient set of variables correctly reflecting the business models of banks and, on the other hand, to find subsets of high similarity. By applying statistical clustering techniques we aim to understand banks’ business models, thereby gaining new insights into the design of the European banking sector and, in particular, identifying peer groups relevant to the top Austrian banks. Assessing the distribution of risk and identifying certain business patterns within those groups allows a meaningful ranking of Austrian banks in comparison to their European competitors.2 The analysis in this paper is conducted on the basis of a purely quantitative methodology and the results should be interpreted accordingly.

Suggested Citation

  • Robert Ferstl & David Seres, 2012. "Clustering Austrian Banks’ Business Models and Peer Groups in the European Banking Sector," Financial Stability Report, Oesterreichische Nationalbank (Austrian Central Bank), issue 24, pages 79-95.
  • Handle: RePEc:onb:oenbfs:y:2012:i:24:b:3
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    References listed on IDEAS

    as
    1. Leisch, Friedrich, 2006. "A toolbox for K-centroids cluster analysis," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 526-544, November.
    2. Manganelli, Simone & Altunbas, Yener & Marqués-Ibáñez, David, 2011. "Bank risk during the financial crisis: do business models matter?," Working Paper Series 1394, European Central Bank.
    3. Emili Tortosa-Ausina, 2002. "Cost Efficiency and Product Mix Clusters across the Spanish Banking Industry," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 20(2), pages 163-181, March.
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    Cited by:

    1. Lagasio, Valentina & Quaranta, Anna Grazia, 2022. "Cluster analysis of bank business models: The connection with performance, efficiency and risk," Finance Research Letters, Elsevier, vol. 47(PA).

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    More about this item

    Keywords

    Austrian banks; cluster analysis; data-driven decision support;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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