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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Authors
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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Abstract
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%.
Keywords
Discriminant Analysis, Logistics Analysis, Financial Distress, Algerian Economic Companies
To cite this article
Nedjema Abbas, Intissar Slimani, 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. doi: 10.11648/j.sjbm.20170502.15
Copyright
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/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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