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A comparison of corporate distress prediction models in Brazil: hybrid neural networks, logit models and discriminant analysis

Author

Listed:
  • Juliana Yim

    (RMIT University)

  • Heather Mitchell

    (RMIT University)

Abstract

This paper looks at the ability of a relatively new technique, hybrid ANN's, to predict corporate distress in Brazil. These models are compared with traditional statistical techniques and conventional ANN models. The results suggest that hybrid neural networks outperform all other models in predicting firms in financial distress one year prior to the event. This suggests that for researchers, policymakers and others interested in early warning systems, hybrid networks may be a useful tool for predicting firm failure.

Suggested Citation

  • Juliana Yim & Heather Mitchell, 2005. "A comparison of corporate distress prediction models in Brazil: hybrid neural networks, logit models and discriminant analysis," Nova Economia, Economics Department, Universidade Federal de Minas Gerais (Brazil), vol. 15(1), pages 73-93, January-A.
    • Handle: RePEc:nov:artigo:v:15:y:2005:i:1:p:73-93
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    Citations

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    Cited by:

    1. Manel Hamdi & Sami Mestiri, 2014. "Bankruptcy prediction for Tunisian firms : An application of semi-parametric logistic regression and neural networks approach," Economics Bulletin, AccessEcon, vol. 34(1), pages 133-143.
    2. Madalina Ecaterina POPESCU & Marin ANDREICA & Ion-Petru POPESCU, 2017. "Decision Support Solution To Business Failure Prediction," Proceedings of the INTERNATIONAL MANAGEMENT CONFERENCE, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 11(1), pages 99-106, November.
    3. Liu, Chang & Sun, Xiaolei & Chen, Jianming & Li, Jianping, 2016. "Statistical properties of country risk ratings under oil price volatility: Evidence from selected oil-exporting countries," Energy Policy, Elsevier, vol. 92(C), pages 234-245.
    4. Philippe Jardin & David Veganzones & Eric Séverin, 2019. "Forecasting Corporate Bankruptcy Using Accrual-Based Models," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 7-43, June.
    5. Lean Yu & Xinxie Li & Ling Tang & Zongyi Zhang & Gang Kou, 2015. "Social credit: a comprehensive literature review," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 1(1), pages 1-18, December.
    6. du Jardin, Philippe, 2010. "Predicting bankruptcy using neural networks and other classification methods: the influence of variable selection techniques on model accuracy," MPRA Paper 44375, University Library of Munich, Germany.
    7. Denis Kušter & Bojana Vuković & Sunčica Milutinović & Kristina Peštović & Teodora Tica & Dejan Jakšić, 2023. "Early Insolvency Prediction as a Key for Sustainable Business Growth," Sustainability, MDPI, vol. 15(21), pages 1-24, October.
    8. Juliana Yim & Heather Mitchell, 2007. "Predicting Financial Distress In The Australian Financial Service Industry," Australian Economic Papers, Wiley Blackwell, vol. 46(4), pages 375-388, December.

    More about this item

    Keywords

    hybrid neural networks; corporate failures;

    JEL classification:

    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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