Market Segmentation with Self-Organizing Maps in Banking Industry

Mehmet Ozcalici

Journal of BRSA Banking and Financial Markets, 2017, vol. 11, issue 2, 9-30

Abstract: Banks play important roles in an economy with their financial intermediary roles. For this reason, supervision of the banking sector is also important. The development of the sector can be monitored with clustering analysis. In this study, self-organizing maps which implements unsupervised learning is applied to cluster 13 commercial banks with branches over 100. The dataset covers 2014-2017 period. 12 financial ratios are calculated from the financial statements of banks. Clustering validity is examined with the help of Silhouette values and graphs. As a result, it was determined that the sector formed three clusters in 2014. In 2015-2017 periods, cluster structures were not changed and banks were collected in two clusters. The technique has the advantage of separating the data set into clusters without the need for user information.

Keywords: Artificial Neural Networks; Self-organizing Maps; Clustering; Segmentation (search for similar items in EconPapers)
JEL-codes: C38 C45 G21 (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:bdd:journl:v:11:y:2017:i:2:p:9-30

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