Comparison Between Discriminant Analysis Model and Logistic Regression Model to Predict the Distress of the Algerian Economic Companies
Science Journal of Business and Management
Volume 5, Issue 2, April 2017, Pages: 70-77
Received: Feb. 6, 2017;
Accepted: Feb. 22, 2017;
Published: Mar. 27, 2017
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Nedjema Abbas, Faculty of Economic, Commercial and Management Sciences, Batna University, Batna, Algeria
Intissar Slimani, Faculty of Economic, Commercial and Management Sciences, Batna University, Batna, Algeria
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This study aims to select the appropriate method the most able to distinguish between distressed and non-distressed economic companies, which will lead us to know the most important variables that can detect the pre-distress for Algerian economic companies, specifically in the Algerian environment, through the comparison between the findings of the application of two of the most important statistical methods, i.e, discriminant and logistic analysis, on a sample of 60 companies; half of them is distressed and the other half is non-distressed according to four financial ratios selected among the financial ratios the most used by researchers, namely: sales to total assets, working capital to total assets, profit before interest and tax and the ratio of equity to total assets. We concluded, by comparing the findings of the discriminant model and logistic model classification, that the latter is more able to distinguish between distressed and non-distressed Algerian economic companies by 96.7%, compared to the findings of the Fischer discriminant model classification by 91.7%.
Discriminant Analysis, Logistics Analysis, Financial Distress, Algerian Economic Companies
To cite this article
Comparison Between Discriminant Analysis Model and Logistic Regression Model to Predict the Distress of the Algerian Economic Companies, Science Journal of Business and Management.
Vol. 5, No. 2,
2017, pp. 70-77.
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
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CRL: Coefficient of racial likness.
Carl J. Huberty and Stephen Olejnik, Applied MANOVA and Discriminant Analysis, (Canada: John Wiley and Sons, 2006), p.3.
Younes Boujelbene and Sihem Khemakhem, " Credit Risk Prediction: A Comparative Study between Discriminant Analysis and the Neural Approach ", p.3, available at: http://arxiv.org/ftp/arxiv/papers/1311/1311.4266.pdf.
Sai Vamshidhar Nudurupati, "Robust Nonparametric Discriminant Analysis Procedures", Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy, Auburn University, 2009, p.1.
Mohamed Dahmani, "Prediction of the Distress of Companies by Scoring Method", End of study dissertation to obtain the higher diploma of Banking studies, 9th class, October 2007, p.40.
Mireille Bardos, " Scoring on Business Data: An individual Diagnostic Instrument and a Portfolio Analysis Tool for a Clientele ", MODULAD Review, No. 38, 2008, p.165.
Joseph M. Hilbe, Logistic regression models, (United state of America: CRC press, 2009), p.3.
Hamadi Matoussi, Rim Mouelhi and Salah Sayah, "The Prediction of bankruptcy of Tunisian Companies by the logistic regression", 20th congress of "AFC", France, 1999, p.07.
Scott A. Czepiel, "Maximum Likelihood Estimation of Logistic Regression Models: Theory and Implementation", p.01. visit http://czep.net/contact.html.
Rajaa Mahmoud Abou Alam, “Statistical Analysis of data using SPSS”, 1st ed., (Egypt: Publishing House for Universities, 2002), p. 224.