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

T. Slavici (), S. Maris and M. Pirtea

Quality & Quantity: International Journal of Methodology, 2016, vol. 50, issue 1, 385-398

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

Keywords: Forecast accuracy; Artificial neural network; Artificial intelligence; Pattern recognition; Bankruptcy prediction (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11135-014-0154-0

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