IDEAS home Printed from https://ideas.repec.org/a/onb/oenbfs/y2012i24b3.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.oenb.at/dam/jcr:9f5fecf1-1624-49ff-8ffd-8a9823115542/fsr_24_special_topics_03_tcm16-252045.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Leisch, Friedrich, 2006. "A toolbox for K-centroids cluster analysis," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 526-544, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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).
    2. Simona Galletta & Sebastiano Mazzù & Valeria Naciti & Carlo Vermiglio, 2021. "Sustainable development and financial institutions: Do banks' environmental policies influence customer deposits?," Business Strategy and the Environment, Wiley Blackwell, vol. 30(1), pages 643-656, January.
    3. Michal Skorepa & Jakub Seidler, 2015. "Capital buffers based on banks’ domestic systemic importance: selected issues," Journal of Financial Economic Policy, Emerald Group Publishing Limited, vol. 7(3), pages 207-220, August.
    4. Milne, Alistair, 2014. "Distance to default and the financial crisis," Journal of Financial Stability, Elsevier, vol. 12(C), pages 26-36.
    5. González, Luis Otero & Razia, Alaa & Búa, Milagros Vivel & Sestayo, Rubén Lado, 2017. "Competition, concentration and risk taking in Banking sector of MENA countries," Research in International Business and Finance, Elsevier, vol. 42(C), pages 591-604.
    6. Amine Ben Amar & Ikrame Ben Slimane & Makram Bellalah, 2017. "Are Non-Conventional Banks More Resilient than Conventional Ones to Financial Crisis?," Working Papers hal-01455752, HAL.
    7. Antoniades, Adonis, 2015. "Commercial bank failures during the Great Recession: the real (estate) story," Working Paper Series 1779, European Central Bank.
    8. Hryckiewicz, Aneta & Kozłowski, Łukasz, 2017. "Banking business models and the nature of financial crisis," Journal of International Money and Finance, Elsevier, vol. 71(C), pages 1-24.
    9. Dima, Bogdan & Dincă, Marius Sorin & Spulbăr, Cristi, 2014. "Financial nexus: Efficiency and soundness in banking and capital markets," Journal of International Money and Finance, Elsevier, vol. 47(C), pages 100-124.
    10. Raslan Alzubi & Mustafa Caglayan & Kostas Mouratidis, 2017. "The Risk-Taking Channel in the US: A GVAR Approach," Working Papers 2017009, The University of Sheffield, Department of Economics.
    11. Jan Kakes & Rob Nijskens, 2018. "Size of the banking sector: implications for financial stability," DNB Occasional Studies 1606, Netherlands Central Bank, Research Department.
    12. Harald Hau & Sam Langfield & David Marques-Ibanez, 2013. "Bank ratings: what determines their quality? [Bank risk during the financial crisis: do business models matter?]," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 28(74), pages 289-333.
    13. Valeria Venturelli & Andrea Landi & Riccardo Ferretti & Stefano Cosma & Elisabetta Gualandri, 2021. "How does the financial market evaluate business models? Evidence from European banks," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 50(2), July.
    14. Juha-Pekka Niinimäki, 2014. "Relationship Lending, Bank Competition and Financial Stability," Czech Economic Review, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, vol. 8(3), pages 102-124, December.
    15. Francesco Cusano & Giuseppe Marinelli & Stefano Piermattei, 2021. "Learning from revisions: a tool for detecting potential errors in banks' balance sheet statistical reporting," Questioni di Economia e Finanza (Occasional Papers) 611, Bank of Italy, Economic Research and International Relations Area.
    16. Grn, Bettina & Leisch, Friedrich, 2009. "Dealing with label switching in mixture models under genuine multimodality," Journal of Multivariate Analysis, Elsevier, vol. 100(5), pages 851-861, May.
    17. Keiichiro Koda & Kazuo Yamada, 2018. "Determinants of Underwriting Fees by New Entrant Banks: Evidence from the Japanese IPO Underwriting Market," Financial Management, Financial Management Association International, vol. 47(2), pages 285-307, June.
    18. Alin Marius Andries & Martin Brown, 2017. "Credit booms and busts in emerging markets," The Economics of Transition, The European Bank for Reconstruction and Development, vol. 25(3), pages 377-437, July.
    19. Mr. Selim A Elekdag & Sheheryar Malik & Ms. Srobona Mitra, 2019. "Breaking the Bank? A Probabilistic Assessment of Euro Area Bank Profitability," IMF Working Papers 2019/254, International Monetary Fund.
    20. Thomas Reutterer & Kurt Hornik & Nicolas March & Kathrin Gruber, 2017. "A data mining framework for targeted category promotions," Journal of Business Economics, Springer, vol. 87(3), pages 337-358, April.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:onb:oenbfs:y:2012:i:24:b:3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Stefan W. Schmitz (email available below). General contact details of provider: https://edirc.repec.org/data/oenbbat.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.