Selecting a kind of financial innovation according to the level of a bank’s financial soundness and its life cycle stage

Abstract

This paper presents the recommendations for selecting a kind of financial innovation in a bank based on the results of theoretical research regarding its usage as a tool for ensuring bank financial soundness. The study is aimed at developing an approach to selecting a kind of financial innovation depending on the level of bank financial soundness and the stage of bank life cycle. The existing method of identifying a bank’s life cycle stage in the framework of the developed approach was improved: it was offered to use the criteria of the growth rates of a bank’s market share, total income, staff costs and net cash flow for grouping banks by the stage of their life cycle and conduct two-steps clustering which helps to determine those banks which are on the transitional stages and to refer a bank to a similar group (growth, stabilization and decline). The empirical results of its implementation suggest that there are three groups of Ukrainian banks that vary according to the stage of bank life cycle (growth, stabilization, decline), excepting those institutions which are on the transitional stages. By the example of banks which represent the main characteristics of each cluster, the authors recommend to launch particular kinds of financial innovation in bank operating activity, taking into account the peculiarities of each group. The empirical results confirm the relevance of the developed approach and its value for identifying the current phase of a bank’s development and managing its financial soundness. Keywords: bank financial soundness, bank life cycle stage, cluster analysis, discriminant analysis, Ukraine. JEL Classification: G21, D91

Application of Clustering in the Dimensionality Reduction Algorithms for Separation of Financial Status of Commercial Banks in Ukraine

“…In our research, we relied on the works of researchers [19][20][21], which are closest to our study, focusing on dividing banks into clusters based on their financial performance indicators. However, they use small sets of indicators that describe mainly one side of the operation of banks.…”

“…However, they use small sets of indicators that describe mainly one side of the operation of banks. In the work of researcher [20] the study is carried out in the context of the Ukrainian market, but it was carried out in 2016 and its results have lost their relevance, since the banking system has undergone changes since then. Besides, given that a system of bank separation, which would be based on a solid economic and mathematical methodology, has not been introduced and enshrined at the legislative level in Ukraine, we made an attempt to expand the studies presented above using the example of Ukrainian banks, involving a wider range of indicators, and propose an author's approach to dividing banks into groups/clusters and analysing their financial condition.…”

“…When selecting a basic set of indicators, the pairwise correlation matrix and the VIF metrics were one of the key indicators for us [31]. This approach is quite popular, and, for example, is used in the work of researcher [20].…”

“…The article by researcher [19] considers the problem of identifying failing banks using neural networks such as self-organizing maps, concluding that "the results show that self-organizing maps can successfully carry out bank clustering tasks and identify banks that require immediate attention from the regulatory bodies" [19]. The work of researcher [20] deals with the clustering of Ukrainian banks using the Ward agglomerative hierarchical clustering, with the aim of dividing them into three groups ("Growth", "Stabilization", "Decline"). Researchers [21] also applied hierarchical cluster analysis to identify and separate problem banks from the rest based on 12 performance indicators.…”

Finansal Perspektifte Modern Bankacılığın Gelişimi

“…Kolodiziev ve arkadaşları Ukrayna'da bankaların finansal sağlamlık düzeyine ve yaşam döngüsü aşamasına göre bir tür finansal yeniliklerin seçilmesini kümeleme analizi ile ele almışlardır. Yazarların yapmış oldukları çalışmada bankaların yaşam döngüleri aşamalarına göre gruplandırılması için bir bankanın pazar payı, toplam gelir, personel maliyetleri ve net nakit akışı büyüme oranları kriterlerinin kullanılması ve belirlenmesine yardımcı olan iki aşamalı kümeleme yapılması önerimişlerdir (Kolodiziev, Chmutova, & Biliaieva, 2016). Karaatlı ve Yıldız Tükiye'de mevduat bankalarının finansal analizini Beklenti Maksimizasyonu Algoritması ile WEKA programında incelemişlerdir (Karaatlı & Yıldız, 2021).…”