Distressed Company Prediction using Logistic Regression: Tunisians Case

Distressed Company Prediction using Logistic Regression: Tunisians Case

Faycal Mraihi

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University of Jendouba

Distressed Company Prediction using Logistic Regression: Tunisians Case

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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.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Faycal Mraihi. 2015. \u201cDistressed Company Prediction using Logistic Regression: Tunisians Case\u201d. Global Journal of Management and Business Research - C: Finance GJMBR-C Volume 15 (GJMBR Volume 15 Issue C3).

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GJMBR Volume 15 Issue C3

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Journal Specifications

Crossref Journal DOI 10.17406/GJMBR

Print ISSN 0975-5853

e-ISSN 2249-4588

Keywords

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Classification

GJMBR-C Classification JEL Code: E60

Submission ReceivedFebruary 11, 2015
Peer Review Double Blind
Handling Editor
Accepted March 5, 2015
Published March 15, 2015

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v1.2

Issue date

April 22, 2015

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Article Matrices

Total Score: 101

Country: Tunisia

Subject: Global Journal of Management and Business Research - C: Finance

Authors: Faycal Mraihi (PhD/Dr. count: 0)

View Count (all-time): 210

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Publish Date: 2015 04, Wed

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Total Views: 4464

Total Downloads: 2240

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