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dc.contributor.authorSood, Ashvin Rajen_NZ
dc.date.available2011-04-07T03:10:10Z
dc.date.copyright1996-10-01en_NZ
dc.identifier.citationSood, A. R. (1996, October 1). Comparing predictor methods; the case of bankruptcy predictions (Dissertation, Bachelor of Commerce with Honours). Retrieved from http://hdl.handle.net/10523/1208en
dc.identifier.urihttp://hdl.handle.net/10523/1208
dc.description.abstractThe aim of this study was to compare three methods of bankruptcy prediction, which were Multivariate Discriminant Analysis, Logit Analysis and Artificial Neural Networks. The methods were compared based on their ability to correctly classify and predict bankruptcy on American public listed companies one year prior to the year of failure. The window of interest in this study was the ten year period from 1984 to 1994. Eleven financial ratios, based on a study by Karels & Prakash (1987), were used in the three prediction methods. It was expected, based on previous literature, that the Artificial Neural Network would be the superior method. This study used a more robust testing technique, incorporating different proportions of bankrupt and financially stable companies as well as different sample mixes between the classification and holdout sample. Overall, it was Multivariate Discriminant Analysis which proved to be the best method of prediction in this study.en_NZ
dc.format.mimetypeapplication/pdf
dc.subjectArtificial Neural Networksen_NZ
dc.subjectbankruptcy predictionen_NZ
dc.subjectMultivariate Discriminant Analysisen_NZ
dc.subject.lcshHF Commerceen_NZ
dc.subject.lcshHF5601 Accountingen_NZ
dc.subject.lcshHG Financeen_NZ
dc.subject.lcshHF5601 Accountingen_NZ
dc.titleComparing predictor methods; the case of bankruptcy predictionsen_NZ
dc.typeDissertationen_NZ
dc.description.versionUnpublisheden_NZ
otago.bitstream.pages97en_NZ
otago.date.accession2006-08-28en_NZ
otago.schoolAccountancy and Business Lawen_NZ
thesis.degree.disciplineAccountancy and Business Lawen_NZ
thesis.degree.nameBachelor of Commerce with Honours
thesis.degree.grantorUniversity of Otagoen_NZ
thesis.degree.levelHonours Dissertationsen_NZ
otago.openaccessOpen
dc.identifier.eprints377en_NZ
otago.school.eprintsAccountancy & Business Lawen_NZ
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