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

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• 作者：T. Slavici ; S. Maris ; M. Pirtea
• 关键词：Forecast accuracy ; Artificial neural network ; Artificial intelligence ; Pattern recognition ; Bankruptcy prediction
• 刊名：Quality & Quantity
• 出版年：2016
• 出版时间：January 2016
• 年：2016
• 卷：50
• 期：1
• 页码：385-398
• 全文大小：1,167 KB
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• 作者单位：T. Slavici (1) (2)
  S. Maris (1)
  M. Pirtea (3)
  
  1. Ioan Slavici University, Str. Prof. Dr. A. Paunescu- Podeanu Nr. 144, Timisoara, Romania
  2. Politehnica University of Timisoara, Bd. V. Parvan Nr. 2, Timisoara, Romania
  3. West University of Timisoara, Bd. V. Parvan Nr. 4, Timisoara, Romania
  
• 刊物类别：Humanities, Social Sciences and Law
• 刊物主题：Social Sciences
  Methodology of the Social Sciences
  Social Sciences
  
• 出版者：Springer Netherlands
• ISSN：1573-7845

文摘

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.