Visual Macroprudential Surveillance of Banks

Sarlin, Peter

doi:10.1002/isaf.1391

Cited by 1 publication

(1 citation statement)

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“…The seminal applications of SOMs in finance‐related literature cover a variety of topics, including bankruptcy prediction (Back et al ., 1995) and financial diagnosis (Deboeck, 1998; Kiviluoto and Bergius, 1998; Serrano‐Cinca, 1996). More recent applications include, for instance, the use of SOMs as a tool for macroprudential supervision (Sarlin, 2016) and the prediction of currency crises (Sarlin and Marghescu, 2011).…”

Section: Clustering Methods and Clustering Ensemblesmentioning

confidence: 99%

Using clustering ensemble to identify banking business models

Marques

Alves

2020

Intell Sys Acc Fin Mgmt

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Summary 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.

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Section: Clustering Methods and Clustering Ensemblesmentioning

confidence: 99%