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

被引：5
|
作者：
Slavici, T. [1 ,2 ]
Maris, S. [1 ]
Pirtea, M. [3 ]
机构：
[1] Ioan Slavici Univ, Str Prof Dr Paunescu Podeanu Nr 144, Timisoara, Romania
[2] Politehn Univ Timisoara, Bd V Parvan Nr 2, Timisoara, Romania
[3] West Univ Timisoara, Bd V Parvan Nr 4, Timisoara, Romania
来源：
QUALITY & QUANTITY | 2016年 / 50卷 / 01期
关键词：
Forecast accuracy; Artificial neural network; Artificial intelligence; Pattern recognition; Bankruptcy prediction;
D O I：
10.1007/s11135-014-0154-0
中图分类号：
C [社会科学总论];
学科分类号：
03 ; 0303 ;
摘要：
Our study aims to present an optimisationmethod 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.
引用
收藏
页码：385 / 398
页数：14
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