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

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 o...

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Autores principales:	Youssef Zizi, Amine Jamali-Alaoui, Badreddine El Goumi, Mohamed Oudgou, Abdeslam El Moudden
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	financial distress prediction logistic regression neural networks feature selection SMEs econometric modeling Insurance HG8011-9999
Acceso en línea:	https://doaj.org/article/9510aa82560c41db8299230f1a50c2ff
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spelling	oai:doaj.org-article:9510aa82560c41db8299230f1a50c2ff2021-11-25T18:56:10ZAn Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression10.3390/risks91102002227-9091https://doaj.org/article/9510aa82560c41db8299230f1a50c2ff2021-11-01T00:00:00Zhttps://www.mdpi.com/2227-9091/9/11/200https://doaj.org/toc/2227-9091In 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.Youssef ZiziAmine Jamali-AlaouiBadreddine El GoumiMohamed OudgouAbdeslam El MouddenMDPI AGarticlefinancial distress predictionlogistic regressionneural networksfeature selectionSMEseconometric modelingInsuranceHG8011-9999ENRisks, Vol 9, Iss 200, p 200 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	financial distress prediction logistic regression neural networks feature selection SMEs econometric modeling Insurance HG8011-9999
spellingShingle	financial distress prediction logistic regression neural networks feature selection SMEs econometric modeling Insurance HG8011-9999 Youssef Zizi Amine Jamali-Alaoui Badreddine El Goumi Mohamed Oudgou Abdeslam El Moudden An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression
description	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.
format	article
author	Youssef Zizi Amine Jamali-Alaoui Badreddine El Goumi Mohamed Oudgou Abdeslam El Moudden
author_facet	Youssef Zizi Amine Jamali-Alaoui Badreddine El Goumi Mohamed Oudgou Abdeslam El Moudden
author_sort	Youssef Zizi
title	An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression
title_short	An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression
title_full	An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression
title_fullStr	An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression
title_full_unstemmed	An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression
title_sort	optimal model of financial distress prediction: a comparative study between neural networks and logistic regression
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/9510aa82560c41db8299230f1a50c2ff
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An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression

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