Artikel

Self-organising (Kohonen) maps for the Vietnam banking industry

This is the first study to use the self-organisation (Kohonen) map technique, an artificial neural network based on a non-supervised learning algorithm, to categorise Vietnamese banks into super-class groups. Drawing on unbalanced yearly data from 2008 to 2017, this study identifies two super-class groups (one and two). While group one consists of joint stock banks, group two consists of commercial state and joint stock banks. Using the non-structural indicator, the Lerner index, to capture market power, and the data enveloped analysis technique to measure bank performance, our result shows significant differences in Lerner scores (which represent bank market power) of the two groups of banks. Differences in the Lerner scores provide evidence of a group of strong banks that is isolated from other banks. This implies that this strong bank group has the potential to be monopolist and impairs Vietnam's competitive banking environment. The reason is that group two banks may be more profitable due to greater market power, whereas group one banks may struggle to cut costs to remain viable. These findings provide a better understanding for bank executives, policymakers and regulators of the Vietnam banking industry, and ensure an efficient and competitive Vietnam banking environment.

Sprache
Englisch

Erschienen in
Journal: Journal of Risk and Financial Management ; ISSN: 1911-8074 ; Volume: 14 ; Year: 2021 ; Issue: 10 ; Pages: 1-18 ; Basel: MDPI

Klassifikation
Wirtschaft
Monetary Policy, Central Banking, and the Supply of Money and Credit: General
Mergers; Acquisitions; Restructuring; Voting; Proxy Contests; Corporate Governance
Thema
self-organisation maps
artificial neural networks
monopolists
market power
Vietnam

Ereignis
Geistige Schöpfung
(wer)
Man Ha
Gan, Christopher
Nguyen, Cuong
Anthony, Patricia
Ereignis
Veröffentlichung
(wer)
MDPI
(wo)
Basel
(wann)
2021

DOI
doi:10.3390/jrfm14100485
Handle
Letzte Aktualisierung
10.03.2025, 10:44 UTC

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Artikel

Beteiligte

  • Man Ha
  • Gan, Christopher
  • Nguyen, Cuong
  • Anthony, Patricia
  • MDPI

Entstanden

  • 2021

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