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Usage of artificial neural networks for optimal bankruptcy forecasting. Case study: Eastern European small manufacturing enterprises

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  • T. Slavici
  • S. Maris
  • M. Pirtea

Abstract

Our study aims to present an optimisation method for the forecasting of bankruptcy. To this end, we elaborate and optimise an artificial neural network (ANN) which, based on the situation of real companies in Eastern Europe, can forecast bankruptcy state. After describing the network structure, the performance is evaluated. Using specific statistical methods, a statistical network optimisation is performed. The conclusion is that ANNs are extremely productive in predicting firm bankruptcy, with the forecast accuracy being higher than the accuracy obtained by traditional methods. The results are applicable at an international level, though the target group of this study contains mainly Eastern European Small Manufacturing Enterprises. Copyright Springer Science+Business Media Dordrecht 2016

Suggested Citation

  • T. Slavici & S. Maris & M. Pirtea, 2016. "Usage of artificial neural networks for optimal bankruptcy forecasting. Case study: Eastern European small manufacturing enterprises," Quality & Quantity: International Journal of Methodology, Springer, vol. 50(1), pages 385-398, January.
    • Handle: RePEc:spr:qualqt:v:50:y:2016:i:1:p:385-398
    DOI: 10.1007/s11135-014-0154-0
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    References listed on IDEAS

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    1. Rebecca Wu, 1997. "Neural network models: Foundations and applications to an audit decision problem," Annals of Operations Research, Springer, vol. 75(0), pages 291-301, January.
    2. Teodora Dogaru & Frank Van Oort & Mark Thissen, 2011. "Agglomeration Economies In European Regions: Perspectives For Objective 1 Regions," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 102(4), pages 486-494, September.
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    Cited by:

    1. Gintare Giriūniene & Lukas Giriūnas & Mangirdas Morkunas & Laura Brucaite, 2019. "A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania," Economies, MDPI, vol. 7(3), pages 1-20, August.
    2. Stefania Corsaro & Valentina De Simone & Zelda Marino & Salvatore Scognamiglio, 2024. "Learning fused lasso parameters in portfolio selection via neural networks," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(5), pages 4281-4299, October.

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