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

Author

Listed:
  • Muriel Perez

    () (COACTIS - COACTIS - 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
    Note: View the original document on HAL open archive server: https://halshs.archives-ouvertes.fr/halshs-00522129
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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. Khaled Halteh & Kuldeep Kumar & Adrian Gepp, 2018. "Using Cutting-Edge Tree-Based Stochastic Models to Predict Credit Risk," Risks, MDPI, Open Access Journal, vol. 6(2), pages 1-13, May.

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