The Bankruptcy Prediction in Tehran Share Holding Using Neural Network and Its Comparison with Logistic Regression

Volume 5, Issue 3, pp 219 - 228 http://dx.doi.org/10.22436/jmcs.05.03.10

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Authors

Mahnaz Bagheri - Department of Mathematic, Islamic Azad University, Behshahr branch, Iran. Mehrdad Valipour - Department of Accounting, Islamic Azad University, Neka branch, Iran. Vahid Amin - Department of Accounting, Payamnour University, Behshahr Branch

Abstract

The use of financial ratios for predicting companies' bankruptcy has always been considered by universities and economical institutions especially banks and other financial organizations. In such studies, statistical models like multiple distinctive analyses (MDA), logit Analysis, probit Analysis have usually been used. In this study, the prediction of accepted productive companies' bankruptcy in Tehran negotiable papers exchange has been paid by the use of artificial neural network (ANN) model and we have also made a comprehensive review on the models of bankruptcy prediction. In this study, artificial neural network model with logistic regression (LR) statistical model that is a useful statistical model in bankruptcy prediction has been compared. Our findings from these models on the basis of 80 companies' data showed that artificial neural network model has more accuracy than logistic regression statistical model in bankruptcy prediction.

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Keywords

• Bankruptcy prediction
• financial ratios
• Logistic Regression (LR)
• Artificial Neural Network (ANN).

MSC

•  68U99
•  68T05
•  91B84

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