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ARPN Journal of Science and Technology >> Volume 7, Issue 1, January 2017

ARPN Journal of Science and Technology


Bankruptcy Prediction Using Artificial Neural Networks Evidences From IRAN Stock Exchange

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Author Mahmoud Samadi Largani, Mohammadreza pourali lakelaye, Meysam Kaviani, Navid Samadi Largani
ISSN 2225-7217
On Pages 562-566
Volume No. 2
Issue No. 6
Issue Date July 01, 2012
Publishing Date July 01, 2012
Keywords Bankruptcy, Artificial Neural Networks, Financial ratios



Abstract

The purpose of this study is to explore the applicability of a form of the artificial neural networks (ANNs) for predicting of financial bankruptcy of the companies in Tehran Stock Exchange. The model is tested against the recursive partitioning algorithm with a data set used in a previously published study. The model is then used with data obtained from the Compact Disclosures TM CD. Statistical methods of research are regression, Diagnostic analysis and artificial neural network. Neural network (NN) used in this type of multi-layer perception is trained using error back propagation algorithm. Sample included two groups of non- bankrupt and bankrupt companies.
The results show that the NN model able to predicted the bankruptcy of companies and model accurately in the detection in bankrupt companies is 82% and 93% of non- bankrupt companies. Generally, accuracy of model for training data is 90% and test data is 90.2 %.


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2012 ARPN Publishers