Development of a Prediction Model of Failure in Tunisian Companies: Comparison between Logistic Regression and Support Vector Machines

Authors

  • Fayçal Mraihi Faculty of Law, Economics and Management of Jendouba, Tunisia
  • Inane Kanzari Faculty of Law, Economics and Management of Jendouba, Tunisia
  • Mohamed Tahar Rajhi Faculty of Economics and Management of Tunisia

Keywords:

distressed firms, forecasting model, logistic regression, support vector machine

Abstract

In this study, we try to develop a model for predicting corporate default based on a logistic regression (logit) and a support vector machine (SVM). The two models are applied to the Tunisian cases. Our sample consists of 212 companies in the various industries (106 'healthy' companies and 106 "distressed" companies) over the period 2005-2010. The results of the use of a battery of 87 ratios showed that 12 ratios can build the model and that liquidity and solvency have more weight than profitability and management in predicting the distress. Despite the slight superiority of the results provided by the logistic model, Both on the original sample and the control one, the results provided by the two models are good either in terms of correct percentage of classification or in terms of stability of discriminating power over time and space.

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Published

2015-12-04

How to Cite

Fayçal Mraihi, Inane Kanzari, & Mohamed Tahar Rajhi. (2015). Development of a Prediction Model of Failure in Tunisian Companies: Comparison between Logistic Regression and Support Vector Machines. International Journal of Empirical Finance, 4(3), 184–205. Retrieved from https://rassorg.com/IJEF/article/view/90

Issue

International Journal of Empirical Finance (IJEF) - Volume 4, Issue 3

Section

Articles