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dc.contributor.authorHa, Minh M.en
dc.contributor.authorGan, Christopheren
dc.contributor.authorNguyen, Cuongen
dc.contributor.authorAnthony, Patriciaen
dc.date.accessioned2021-11-18T20:32:14Z
dc.date.available2021-10-13en
dc.date.issued2021-10en
dc.date.submitted2021-10-09en
dc.identifier.issn1911-8074en
dc.identifier.urihttps://hdl.handle.net/10182/14415
dc.description.abstractThis 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.en
dc.format.extent18en
dc.languageenen
dc.language.isoenen
dc.publisherMDPIen
dc.relationThe original publication is available from - MDPI - https://doi.org/10.3390/jrfm14100485en
dc.relation.urihttps://doi.org/10.3390/jrfm14100485en
dc.rights© 2021 by the authors. Licensee MDPI, Basel, Switzerland.en
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectartificial neural networksen
dc.subjectmonopolistsen
dc.subjectself-organisation mapsen
dc.subjectVietnamen
dc.subjectmarket poweren
dc.titleSelf-organising (Kohonen) maps for the Vietnam banking industryen
dc.typeJournal Article
lu.contributor.unitLincoln Universityen
lu.contributor.unitFaculty of Agribusiness and Commerceen
lu.contributor.unitDepartment of Financial and Business Systemsen
lu.contributor.unitFaculty of Environment, Society and Designen
lu.contributor.unitSchool of Landscape Architectureen
dc.identifier.doi10.3390/jrfm14100485en
dc.relation.isPartOfJournal of Risk and Financial Managementen
pubs.issue10en
pubs.organisational-group/LU
pubs.organisational-group/LU/Faculty of Agribusiness and Commerce
pubs.organisational-group/LU/Faculty of Agribusiness and Commerce/FABS
pubs.organisational-group/LU/Faculty of Environment, Society and Design
pubs.organisational-group/LU/Faculty of Environment, Society and Design/SOLA
pubs.organisational-group/LU/Research Management Office
pubs.organisational-group/LU/Research Management Office/PE20
pubs.organisational-group/LU/Research Management Office/QE18
pubs.publication-statusPublisheden
pubs.volume14en
dc.identifier.eissn1911-8074en
dc.rights.licenceAttributionen
lu.identifier.orcid0000-0002-5618-1651
lu.identifier.orcid0000-0002-7563-2374
lu.identifier.orcid0000-0002-4991-3340
pubs.article-number485en
dc.subject.anzsrc2020380107 Financial economicsen
dc.subject.anzsrc2020380204 Panel data analysisen
dc.subject.anzsrc2020350204 Financial institutions (incl. banking)en
dc.subject.anzsrc2020350301 Business analyticsen


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