Using Clustering Ensemble to Identify Banking Business Models

Intelligent Systems in Accounting, Finance and Management, Forthcoming

DOI: 10.1002/isaf.1471

51 Pages Posted: 3 Jun 2020 Last revised: 16 Nov 2020

See all articles by Bernardo P. Marques

Bernardo P. Marques

Católica Porto Business School; University of Porto, Faculty of Economics

Carlos F. Alves

University of Porto - Faculty of Economics

Date Written: March 5, 2020

Abstract

The business models of banks are often seen as the result of a variety of simultaneously determined managerial choices, such as those regarding the types of activities, funding sources, level of diversification, and size. Moreover, owing to the fuzziness of data and the possibility that some banks may combine features of different business models, the use of hard clustering methods has often led to poorly identified business models. In this paper we propose a framework to deal with these challenges based on an ensemble of three unsupervised clustering methods to identify banking business models: fuzzy c-means (which allows us to handle fuzzy clustering), self-organizing maps (which yield intuitive visual representations of the clusters), and partitioning around medoids (which circumvents the presence of data outliers). We set up our analysis in the context of the European banking sector, which has seen its regulators increasingly focused on examining the business models of supervised entities in the aftermath of the twin financial crises. In our empirical application, we find evidence of four distinct banking business models and further distinguish between banks with a clearly defined business model (core banks) and others (non-core banks), as well as banks with a stable business model over time (persistent banks) and others (non-persistent banks). Our proposed framework performs well under several robustness checks related with the sample, clustering methods, and variables used.

Keywords: Banking, Business Models, Clustering Ensemble, Fuzzy Clustering, Self-Organizing Maps

JEL Classification: G20, G21, G28, G32

Suggested Citation

Marques, Bernardo P. and Alves, Carlos Francisco Ferreira, Using Clustering Ensemble to Identify Banking Business Models (March 5, 2020). Intelligent Systems in Accounting, Finance and Management, Forthcoming, DOI: 10.1002/isaf.1471, Available at SSRN: https://ssrn.com/abstract=3593311

Bernardo P. Marques (Contact Author)

Católica Porto Business School ( email )

Rua de Diogo Botelho 1327
Porto, Porto 4169-005
Portugal

University of Porto, Faculty of Economics ( email )

Rua Roberto Frias
s/n
Porto, 4200-464
Portugal

Carlos Francisco Ferreira Alves

University of Porto - Faculty of Economics ( email )

Rua Roberto Frias
s/n
Porto, 4200-464
Portugal
+351 225571242 (Phone)
+351 225505050 (Fax)

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