Insolvency prediction model using multivariate discriminant analysis and artificial neural network for the finance industry in New Zealand
Chung, Kim-Choy; Tan, Shin Shin; Holdsworth, David K
Models 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.
Keywords: corporate insolvency; financial ratios; Multivariate Discriminant Analysis; Artificial Neural Networks
Research Type: Journal Article
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