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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  • Altman, E. I., Marco, G., & Varetto, F. (1994). Corporate distress diagnosis: Comparison using linear discriminant analysis and neural networks (the Italian experience). Journal of Banking and Finance, 18(3), 103.

  • Hair, J. F. Jr., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate Data Analysis (6th ed.). Upper Saddle River, NJ: Pearson/Prentice Hall.

  • Javanmard, H., & Saleh, F. (2009). The comparison artificial neural networks and multi decimal analysis models for forecasting bankruptcy and financial distress. Proceedings of the World Congress on Engineering, 2

  • Lin, T. H. (2009). A cross model study of corporate financial distress prediction in Taiwan: Multiple discriminant analysis, logit, probit and neural networks models. Neurocomputing, 72(16-18), 3507-3516.

  • Luther, K. R. (1994). An Artificial Neural Network Approach to Predicting the Outcome of Chapter 11 Bankruptcy. Journal of Business and Economic Studies, 4(1).

  • Nargundkar, R. Marketing Research (3rd ed.). New Delhi: McGraw-Hill.

  • Ohlson, J. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18(1), 109-31.

  • Registered cases for bankruptcy. (2011, Jan 25). Retrieved from http://bifr.nic.in/casesregd.htm

  • Wang, Y., & Campbell, M. (2010). Financial ratios and the prediction of bankruptcy: The Ohlson model applied to Chinese publicly traded companies. Journal of Organisational Leadership and Business.

  • Zhang, G., Hu, Y. M., Patuwo, E. B., & Indro, C. D. (1999). Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis. European Journal of Operations Research, 116(1), 16-32.

  • Zurada, J. M. (1996). Introduction to Artificial Neural Systems. Mumbai: Jaico Publishing House.


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

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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