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Improving bankruptcy prediction with Hidden Layer Learning Vector Quantization

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  • J. C. Neves
  • A. Vieira

Abstract

A Hidden Layer Learning Vector Quantization (HLVQ), neural network-learning algorithm is used for correcting the outputs of Multilayer Perceptrons (MLP) for predicting corporate bankruptcy. We call this method HLVQ-C, and it is shown that it outperforms both discriminant analysis and traditional neural networks while significantly reducing type I error, which is the type of error that has the highest costs for banks. Moreover, our approach gives an estimation of the prediction robustness thus providing a useful measure of credit risk, which is of great interest for banks, insurance companies and creditors in general. We also show that unbalanced samples, containing more financially sound firms than bankrupt firms, place a strong bias on the classifiers thus leading to a deterioration of type I error accuracy. Although many studies have been published on bankruptcy prediction using neural networks or discriminant analysis, they used mainly US or UK samples of very limited size. Our study is based on industrial French firms, uses a data-set of 583 bankrupt firms over the period 1998-2000 and tests the effects of different proportions of non-bankrupt firms in the sample. Attention was also given to feature selection to reduce the dimensionality of the problem.

Suggested Citation

  • J. C. Neves & A. Vieira, 2006. "Improving bankruptcy prediction with Hidden Layer Learning Vector Quantization," European Accounting Review, Taylor & Francis Journals, vol. 15(2), pages 253-271.
  • Handle: RePEc:taf:euract:v:15:y:2006:i:2:p:253-271
    DOI: 10.1080/09638180600555016
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    Cited by:

    1. Situm Mario, 2014. "Inability of Gearing-Ratio as Predictor for Early Warning Systems," Business Systems Research, Sciendo, vol. 5(2), pages 23-45, September.
    2. Liébana-Cabanillas, F. & Lara-Rubio, J., 2017. "Predictive and explanatory modeling regarding adoption of mobile payment systems," Technological Forecasting and Social Change, Elsevier, vol. 120(C), pages 32-40.
    3. Korol, Tomasz, 2013. "Early warning models against bankruptcy risk for Central European and Latin American enterprises," Economic Modelling, Elsevier, vol. 31(C), pages 22-30.
    4. Tamás Kristóf & Miklós Virág, 2020. "A Comprehensive Review of Corporate Bankruptcy Prediction in Hungary," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 13(2), pages 1-20, February.
    5. Mselmi, Nada & Lahiani, Amine & Hamza, Taher, 2017. "Financial distress prediction: The case of French small and medium-sized firms," International Review of Financial Analysis, Elsevier, vol. 50(C), pages 67-80.
    6. Philippe Jardin, 0. "Forecasting bankruptcy using biclustering and neural network-based ensembles," Annals of Operations Research, Springer, vol. 0, pages 1-36.
    7. Mohammad Mahdi Mousavi & Jamal Ouenniche, 2018. "Multi-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributions," Annals of Operations Research, Springer, vol. 271(2), pages 853-886, December.

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