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Comparison of Bankruptcy Prediction Models: Evidence from India

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  • Varadraj Bapat
  • Abhay Nagale

Abstract

The purpose of this paper is to develop and compare the performance of bankruptcy prediction models using multiple discriminant analysis, logistic regression and neural network for listed companies in India. Accordingly bankruptcy prediction models are developed, over the three years prior to bankruptcy using financial ratios. The sample consists of 72 bankrupt and 72 non-bankrupt companies over the period 1991-2013. The results indicate that compared to multiple discriminant analysis and logistic regression, neural network has the highest classification accuracy for all the three years prior to bankruptcy. This study will be useful to financial institutions, investors, creditors and auditors to identify companies that are likely to experience bankruptcy.

Suggested Citation

  • Varadraj Bapat & Abhay Nagale, 2014. "Comparison of Bankruptcy Prediction Models: Evidence from India," Accounting and Finance Research, Sciedu Press, vol. 3(4), pages 1-91, August.
    • Handle: RePEc:jfr:afr111:v:3:y:2014:i:4:p:91
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    References listed on IDEAS

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    1. Zhang, Guoqiang & Y. Hu, Michael & Eddy Patuwo, B. & C. Indro, Daniel, 1999. "Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis," European Journal of Operational Research, Elsevier, vol. 116(1), pages 16-32, July.
    2. Pamela K. Coats & L. Franklin Fant, 1993. "Recognizing Financial Distress Patterns Using a Neural Network Tool," Financial Management, Financial Management Association, vol. 22(3), Fall.
    3. Grunert, Jens & Norden, Lars & Weber, Martin, 2005. "The role of non-financial factors in internal credit ratings," Journal of Banking & Finance, Elsevier, vol. 29(2), pages 509-531, February.
    4. Neophytou, E. & Charitou, A. & Charalambous, C., 2001. "Predicting Corporate Failure: Empirical Evidence for the UK," Papers 01-173, University of Southampton - Department of Accounting and Management Science.
    5. Andreas Charitou & Evi Neophytou & Chris Charalambous, 2004. "Predicting corporate failure: empirical evidence for the UK," European Accounting Review, Taylor & Francis Journals, vol. 13(3), pages 465-497.
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    Cited by:

    1. Sunaina Kanojia & Shasta Gupta, 2023. "Bankruptcy in Indian context: perspectives from corporate governance," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 27(2), pages 505-545, June.
    2. Javed Iqbal & Furrukh Bashir & Rashid Ahmad & Hina Arshad, 2022. "Predicting Bankruptcy through Neural Network:Case of PSX Listed Companies," iRASD Journal of Management, International Research Alliance for Sustainable Development (iRASD), vol. 4(2), pages 299-315, june.

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    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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