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dc.contributor.authorChung, Kim-Choyen_NZ
dc.contributor.authorTan, Shin Shinen_NZ
dc.contributor.authorHoldsworth, David Ken_NZ
dc.identifier.citationChung, K.-C., Tan, S. S., & Holdsworth, D. K. (2008). Insolvency prediction model using multivariate discriminant analysis and artificial neural network for the finance industry in New Zealand. International Journal of Business and Management, 3(1), 19–29.en
dc.descriptionFull text available through the Social Science Research Network; follow the link provided.en_NZ
dc.description.abstractModels of insolvency are important for managers who may not appreciate how serious the financial health of their company is becoming until it is too late to take effective action. Multivariate discriminant analysis and artificial neural network are utilized in this study to create an insolvency predictive model that could effectively predict any future failure of a finance company and validated in New Zealand. Financial ratios obtained from corporate balance sheets are used as independent variables while failed/non-failed company is the dependent variable. The results indicate the financial ratios of failed companies differ significantly from non-failed companies. Failed companies were also less profitable and less liquid and had higher leverage ratios and lower quality assets.en_NZ
dc.relation.ispartofInternational Journal of Business and Managementen_NZ
dc.subjectcorporate insolvencyen_NZ
dc.subjectfinancial ratiosen_NZ
dc.subjectMultivariate Discriminant Analysisen_NZ
dc.subjectArtificial Neural Networksen_NZ
dc.subject.lcshHF Commerceen_NZ
dc.subject.lcshHF5601 Accountingen_NZ
dc.titleInsolvency prediction model using multivariate discriminant analysis and artificial neural network for the finance industry in New Zealanden_NZ
dc.typeJournal Articleen_NZ
otago.openaccessAbstract Only
dc.description.refereedPeer Revieweden_NZ
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