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2016 | OriginalPaper | Chapter

4. Banks’ Business Models in Europe

Authors : Rym Ayadi, Willem Pieter de Groen

Published in: The Palgrave Handbook of European Banking

Publisher: Palgrave Macmillan UK

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Abstract

This chapter, which expands on Ayadi et al. (Business Models in European Banking: A pre-and post-crisis screening. Brussels: Centre for European Policy Studies (CEPS), 2011), examines the diversity of banks in Europe by identifying banks’ business models using a novel definition and methodology. Some 147 European banks are analysed, which accounts for more than 80 % of total assets during the period 2006–13. The results on the structural and financial attributes, the interaction with ownership structure, the extent of internationalization and the degree of migration are provided for each business model.

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previous chapter Bank Diversification and Financial Conglomerates in Europe

next chapter Ownership in European Banking

See Acharya et al. (2013), Blundell-Wignall et al. (2008), Brunnermeier (2009), Dewatripont et al. (2010), Gorton and Metrick (2012), Hellwig (2009), Reinhajrt and Rogoff (2009), etc.

Alternative instrument combinations were also considered. In many cases, using a different set of instruments led to an unrealistically large number of clusters, with many comprising a single bank/year. Removing any one of the six indicators from the clustering exercise also led to an indistinct clustering. In turn, using a larger set did not change the results substantially, as long as the named indicators were included.

Total derivative exposures are defined as the summation of positive and negative fair values of all derivative transactions, including interest, currency, equity, over the counter (OTC), hedge and trading derivatives.

Except for Norwegian DnB NOR Bank, all banking groups and banks are domiciled in the EU. These EU banking groups and banks had total assets of €34.7 trillion in 2012. Hence, the sample represents around 80 % of the EU total banking assets (€43.6 trillion), using ECB (2014) consolidated banking data. The sample covers at least 50 % of the banking assets in each of the EU member states.

See Ayadi and De Groen (2014b) for a comprehensive overview of the banks subject to the CEBS EU-Wide Stress Testing Exercise in 2010 and the 2011 EU-wide stress test, EU Capital exercise 2011, and 2013 EU-wide transparency exercise conducted by EBA. See https://www.eba.europa.eu/documents/10180/669262/Methodological+Note.pdf for the sample of the 2014 EU-banks stress test.

See http://www.ecb.europa.eu/pub/pdf/other/en_dec_2014_03_fen.pdf?21d953cb19106056a509a22888c646a8 for the full list of credit institutions that is subject to the first comprehensive assessment of the European Central Bank (ECB). The subsidiaries of banking groups included in the CEBS/EBA exercises are assessed at the group level, e.g. to avoid double counting.

See http://www.financialstabilityboard.org/publications/r_131111.pdf for the GSIBs-list as of November 2013.

All the instruments used for clustering were standardized so that each indicator had a mean of zero and a standard deviation of one. This was done to prevent any potential biases arising from the choice of units, i.e. use of percentages rather than basis points.

See Milligan (1981) and references therein for an assessment of different clustering methods.

Evaluating a variety of cluster stopping rules, Milligan and Cooper (1985) single out the Calinski and Harabasz index as the best and most consistent rule, identifying the sought configurations correctly in over 90 % of all cases in simulations.

The model was computed in close collaboration with HEC Montreal through its Observatory on Financial Services Cooperatives and the International Initiative for Sustainable Financial Systems (ISFS) under the International Institute of Cooperatives Alphonse and Dorimène-Desjardins (IICADD).

See Everitt et al. (2001) for a highly readable introduction to cluster analysis and some of the practical issues in the choice of technical procedures.

Three-quarters of the banks included in Ayadi et al. (2012) have been identified in the same cluster in this exercise. 85 % to 90 % of the banks identified as investment, diversified retail or focused retail in the previous study have been identified as such again. In turn, a large share of the formerly identified wholesale banks are now identified as investment banks.

The group of banks identified as wholesale banks have changed substantially from Ayadi et al. (2012). In particular, only 41 % bank/year observations identified as wholesale banks in the earlier study were identically grouped here. An important explanation for this might be the fact that the number of banks and years covered have been increased, which has changed the composition of the sample.

The public data on these local banks is often scarce. Many of the local banks are rather small and therefore have to comply with less extensive reporting requirements. This makes the analysis of this group of banks, which make up a significant part of the banking sector, more challenging.

See Appendix I for a complete list of banks surveyed, grouped by business model.

An analysis of the year-by-year transitions (not provided here) shows that the transitions from the investment and diversified retail to the focused retail model were particularly high in 2011, in the midst of the Eurozone crisis when non-deposit funding was more difficult to attract.

Acharya, V. V., Schnabl, P., & Suarez, G. (2013). Securitization without risk transfer. Journal of Financial Economics, 107(3), 515–536.CrossRef

Ayadi, R., & De Groen, W. P. (2014a). Banking business models monitor—2014. Montreal: CEPS and IOFSC.

Ayadi, R., & de Groen, W. P. (2014b). Stress testing, transparency and uncertainty in European banking: What impacts? In J. Forssbaek & L. Oxelheim (Eds.), The Oxford handbook of economic and institutional transparency. New York: Oxford University Press.

Ayadi, R., Llewelyn, D. T., Schmidt, R. H., Arbak, E., & de Groen, W. P. (2010). Investigating diversity in the banking sector in Europe: Key developments, performance and role of cooperative banks. Brussels: Centre for European Policy Studies (CEPS).

Ayadi, R., Arbak, E., & de Groen, W. P. (2011). Business models in European banking: A pre-and post-crisis screening. Brussels: Centre for European Policy Studies (CEPS).

Ayadi, R., Arbak, E., & de Groen, W. P. (2012). Regulation of European banks and business models: Towards a new paradigm? Brussels: Centre for European Policy Studies (CEPS).

Blundell-Wignall, A., Atkinson, P., & Lee, S. G. (2008). The current financial crisis: Causes and policy issues. InOECD Financial Market Trends. Paris: OECD.

Brunnermeier, M. K. (2009). Symposium: Early stages of the credit crunch: Deciphering the liquidity and credit crunch 2007–2008. Journal of Economic Perspectives, 23(1), 77–100.CrossRef

Calinski, R. B., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics, 3(1), 1–27.

Dewatripont, M., Rochet, J.-C., & Tirole, J. (2010). Balancing the banks: Global lessons from the financial crisis. Princeton/Oxford: Princeton University Press.CrossRef

Everitt, B. S., Landau, S., & Leese, M. (2001). Cluster analysis (4 ed.). West Sussex: Wiley, John & Sons Ltd..

Gorton, G. B., & Metrick, A. (2012). Securitized banking and the run on repo. Journal of Financial Economics, 104(3), 425–451.CrossRef

Hellwig, M. (2009). Systemic risk in the financial sector: An analysis of the subprime-mortgage financial crisis. De Economist, 157(2), 129–207.CrossRef

Milligan, G. W. (1981). A review of Monte Carlo tests of cluster analysis. Multivariate Behavioral Research, 16(3), 379–407.

Milligan, G. W., & Cooper, M. C. (1985). An examination of procedures for determining the number of clusters in a data set. Psychometrika, 50(2), 159–179.CrossRef

Reinhart, C. M., & Rogoff, K. (2009). This time is different: Eight centuries of financial folly. Princeton: Princeton University Press.

Ward, J. H. (1963). Hierarchical grouping to optimize objective function. Journal of the American Statistical Association, 58(301), 236–244.CrossRef

Title: Banks’ Business Models in Europe
Authors: Rym Ayadi
Willem Pieter de Groen
Publisher: Palgrave Macmillan UK
Book: The Palgrave Handbook of European Banking
Print ISBN: 978-1-137-52143-9

Electronic ISBN: 978-1-137-52144-6

Copyright Year: 2016
DOI: https://doi.org/10.1057/978-1-137-52144-6_4