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Artificial Neural Networks And Bankruptcy Forecasting : A State Of The Art

Author

Listed:
  • Muriel Perez

    (COACTIS - COnception de l'ACTIon en Situation - UL2 - Université Lumière - Lyon 2 - UJM - Université Jean Monnet - Saint-Étienne)

Abstract

The use of neural networks in finance began by the end of the 1980s and by the beginning of the 1990s, it developed specific applications related to forecasting on the failure of companies. In order to highlight the evolution of this research stream, we have retained and analysed 30 studies in which the authors use neural networks to solve companies' classification problems (healthy and failing firms). This review of all these works gives us the opportunity to stress upon future trends in bankruptcy forecasting research

Suggested Citation

  • Muriel Perez, 2006. "Artificial Neural Networks And Bankruptcy Forecasting : A State Of The Art," Post-Print halshs-00522129, HAL.
    • Handle: RePEc:hal:journl:halshs-00522129
    DOI: 10.1007/s00521-005-0022-x
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    Citations

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    Cited by:

    1. Alessandra Amendola & Francesco Giordano & Maria Lucia Parrella & Marialuisa Restaino, 2017. "Variable selection in high‐dimensional regression: a nonparametric procedure for business failure prediction," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 33(4), pages 355-368, August.
    2. Na Luo & Jiayi Yang & Yuanfeng Zhu & Yu Zhang, 2016. "The Risk Management of Commercial Banks¡ª¡ªCredit-Risk Assessment of Enterprises," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 8(9), pages 69-77, September.
    3. Tomasz Korol, 2020. "Assessment of Trajectories of Non-bankrupt and Bankrupt Enterprises," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 1113-1135.
    4. Yajiao Tang & Junkai Ji & Yulin Zhu & Shangce Gao & Zheng Tang & Yuki Todo, 2019. "A Differential Evolution-Oriented Pruning Neural Network Model for Bankruptcy Prediction," Complexity, Hindawi, vol. 2019, pages 1-21, August.
    5. Khaled Halteh & Kuldeep Kumar & Adrian Gepp, 2018. "Using Cutting-Edge Tree-Based Stochastic Models to Predict Credit Risk," Risks, MDPI, vol. 6(2), pages 1-13, May.
    6. Foo See Liang & Shaak Pathak, 2019. "Understanding the Connection of Performance and Z-Scores for Manufacturing Firms in South Korea," Journal of Asian Development, Macrothink Institute, vol. 5(3), pages 37-46, November.
    7. Fioramanti, Marco, 2008. "Predicting sovereign debt crises using artificial neural networks: A comparative approach," Journal of Financial Stability, Elsevier, vol. 4(2), pages 149-164, June.

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