Abstract
The main purpose of this study is to identify which financial ratios are significantly important in predicting distressed companies. This study applied the discriminant analysis on a sample of 28 distressed and 28 healthy firms listed in the Bursa Malaysia during the period from the year 2004 to 2008. The distressed companies represented by Practice Note 17 companies (PN17) and the healthy companies are matched belonged to the same industries classification and have the closest assets. Through the usage of multivariate test, it is found that the financial ratios are able to discriminate the two groups (healthy and distress situation). The model appeared to be fairly accurate with classification accuracy rate more than 70 % up to 3 years before distressed. In addition, Return on Assets is the most important predictor of financial distress for three consecutive years.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wu, D., Liang, L., & Yang, Z. (2008). Analyzing the financial distress of Chinese public companies using probabilistic neural networks and multivariate discriminant analysis. Journal of Socio-economic Planning Sciences, 42, 206–220.
Khoon, G. S., & Hui, L. M. (2009). The impact of the global financial crisis: The case of Malaysia. Available from World Wide Web: http://www.twnside.org.sg/title2/par/impact.of.gfc.on.malaysia-final.dec409.pdf. Accessed 11 Feb 2010.
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23, 589–609.
Sori, Z. M. (2006). Prediction of corporate financial distress: Evidence from Malaysian listed firms during the Asian financial crisis. Available from World Wide Web: http://ssrn.com/abstract=596607. Accessed 12 Feb 2010.
Abdullah, N. A., Halim, A., Ahmad, H., & Rus, R. M. (2008). Predicting corporate failure of Malaysia’s listed companies: Comparing multiple discriminant analysis, logistic regression and the hazard model. Journal of Finance and Economics, 15, 201–217.
Karacaer, S., & Kapusuzoglo, A. (2008). An analysis of the effect of financial ratios on financial situation of Turkish Enterprises resulting from their annual operations. Journal of Finance and Economics, 19, 139–149.
Cooley, W. W., & Lohnes, P. R. (1971). Multivariate data analysis. New York: Wiley.
Gu, Z. (2002). Analyzing bankruptcy in the restaurant industry: A multiple discriminant model. Journal of Hospitality Management, 21, 25–42.
Vuran, B. (2009). Prediction of business failure: A comparison of discriminant analysis and logistic regression analysis. Journal of School of Business Administration, 38, 47–65.
Wuensch, K. L. (2008). Two group discriminant function analysis. Available from World Wide Web: http://www.docstoc.com/docs/21107209/Two-Group-Discriminant-Function-Analysis/. Accessed 24 Mar 2010
Montgomery, D. C., Peck, E. A., & Vining, G. G. (2001). Introduction to linear regression analysis (3rd ed.). New York: Wiley.
Idris, N. F. (2008). Financial ratios as the predictor of corporate distress in Malaysia. MBA thesis, University of Malaya.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media Singapore
About this paper
Cite this paper
Ahmad, N., Rahman, R.A., Jamaluddin, S.H., Azizan, N.H., Shafie, S.N.M. (2014). Discriminant Analysis: A Study on Corporate Distress. In: Kasim, A., Wan Omar, W., Abdul Razak, N., Wahidah Musa, N., Ab. Halim, R., Mohamed, S. (eds) Proceedings of the International Conference on Science, Technology and Social Sciences (ICSTSS) 2012. Springer, Singapore. https://doi.org/10.1007/978-981-287-077-3_24
Download citation
DOI: https://doi.org/10.1007/978-981-287-077-3_24
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-287-076-6
Online ISBN: 978-981-287-077-3
eBook Packages: Humanities, Social Sciences and LawSocial Sciences (R0)