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An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression

Author

Listed:
  • Youssef Zizi

    (Laboratory of Research in Organizational Management Sciences, ENCG Kenitra, Ibn Tofail University, Kenitra 14000, Morocco)

  • Amine Jamali-Alaoui

    (Faculty of Science and Technology, Sidi Mohammed Ben Abdellah University, Fez 3000, Morocco)

  • Badreddine El Goumi

    (INSA EUROMED, University EUROMED of Fez, Fez 3000, Morocco)

  • Mohamed Oudgou

    (ENCG Béni Mellal, University Sultane Moulay Slimane, Béni Mellal 23000, Morocco)

  • Abdeslam El Moudden

    (Laboratory of Research in Organizational Management Sciences, ENCG Kenitra, Ibn Tofail University, Kenitra 14000, Morocco)

Abstract

In the face of rising defaults and limited studies on the prediction of financial distress in Morocco, this article aims to determine the most relevant predictors of financial distress and identify its optimal prediction models in a normal Moroccan economic context over two years. To achieve these objectives, logistic regression and neural networks are used based on financial ratios selected by lasso and stepwise techniques. Our empirical results highlight the significant role of predictors, namely interest to sales and return on assets in predicting financial distress. The results show that logistic regression models obtained by stepwise selection outperform the other models with an overall accuracy of 93.33% two years before financial distress and 95.00% one year prior to financial distress. Results also show that our models classify distressed SMEs better than healthy SMEs with type I errors lower than type II errors.

Suggested Citation

  • Youssef Zizi & Amine Jamali-Alaoui & Badreddine El Goumi & Mohamed Oudgou & Abdeslam El Moudden, 2021. "An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression," Risks, MDPI, vol. 9(11), pages 1-24, November.
    • Handle: RePEc:gam:jrisks:v:9:y:2021:i:11:p:200-:d:674179
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    References listed on IDEAS

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