International Journal of Business Analytics and Intelligence

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Comparison of Logistic Regression and Artificial Neural Network Based Bankruptcy Prediction Models


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1 Jain University, Bangalore, Karnataka, India
     

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Logistic Regression is one of the popular techniques used for bankruptcy prediction and its popularity is attributed due to its robust nature in terms of data characteristics. Recent developments have explored Artificial Neural Networks for bankruptcy prediction. In this study, a paired sample of 174 cases of Indian listed manufacturing companies have been used for building bankruptcy prediction models based on Logistic Regression and Artificial Neural Networks. The time period of study was year 2000 through year 2009. The classification accuracies have been compared for built models and for hold-out sample of 44 paired cases. In analysis and hold-out samples, both the models have shown appreciable classification results, three years prior to bankruptcy. Thus, both the models can be used (by banks, SEBI etc.) for bankruptcy prediction in Indian Context; however, Artificial Neural Network has shown marginal supremacy over Logistic Regression.

Keywords

Bankruptcy, Logistic Regression, Artificial Neural Network, Classification Accuracy.
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  • Comparison of Logistic Regression and Artificial Neural Network Based Bankruptcy Prediction Models

Abstract Views: 466  |  PDF Views: 0

Authors

Easwaran Iyer
Jain University, Bangalore, Karnataka, India
Vinod Kumar Murti
Jain University, Bangalore, Karnataka, India

Abstract


Logistic Regression is one of the popular techniques used for bankruptcy prediction and its popularity is attributed due to its robust nature in terms of data characteristics. Recent developments have explored Artificial Neural Networks for bankruptcy prediction. In this study, a paired sample of 174 cases of Indian listed manufacturing companies have been used for building bankruptcy prediction models based on Logistic Regression and Artificial Neural Networks. The time period of study was year 2000 through year 2009. The classification accuracies have been compared for built models and for hold-out sample of 44 paired cases. In analysis and hold-out samples, both the models have shown appreciable classification results, three years prior to bankruptcy. Thus, both the models can be used (by banks, SEBI etc.) for bankruptcy prediction in Indian Context; however, Artificial Neural Network has shown marginal supremacy over Logistic Regression.

Keywords


Bankruptcy, Logistic Regression, Artificial Neural Network, Classification Accuracy.

References