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Distressed Company Prediction Using Logistic Regression: Tunisian’s Case

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  • Fayçal Mraihi
Abstract
In this study, we try to develop a model for predicting corporate default based on a logistic regression (logit) and applied to the case of Tunisia. Our sample consists of 212 companies in the various industries (106 companies healthy and 106 companies "distressed") 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. Both on the original sample and the control one, these results are good either in terms of correct percentage of classification or in terms of stability of discriminating power over time (on, two and three years before the distress) and space.

Suggested Citation

  • Fayçal Mraihi, 2016. "Distressed Company Prediction Using Logistic Regression: Tunisian’s Case," Quarterly Journal of Business Studies, Research Academy of Social Sciences, vol. 2(1), pages 34-54.
    • Handle: RePEc:rss:jnljbs:v2i1p3
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    References listed on IDEAS

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