Insolvency Prediction Model Using Multivariate Discriminant Analysis and Artificial Neural Network for the Finance Industry in New Zealand

International Journal of Business and Management, Vol. 39, No. 1, pp. 19-28, 2008

10 Pages Posted: 3 Jan 2008  

Kim Choy Chung

American University in Dubai

Shin Shin Tan

Polson Higgs

David K. Holdsworth

University of Otago

Abstract

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

JEL Classification: G33, M41, M47

Suggested Citation

Chung, Kim Choy and Tan, Shin Shin and Holdsworth, David K., Insolvency Prediction Model Using Multivariate Discriminant Analysis and Artificial Neural Network for the Finance Industry in New Zealand. International Journal of Business and Management, Vol. 39, No. 1, pp. 19-28, 2008. Available at SSRN: https://ssrn.com/abstract=1080430

Kim Choy Chung (Contact Author)

American University in Dubai ( email )

Dubai, UAE 28282
United Arab Emirates

Shin Shin Tan

Polson Higgs ( email )

139 Moray place
Dunedin
New Zealand

David K. Holdsworth

University of Otago ( email )

P.O. Box 56
Dunedin, Otago
New Zealand

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