Compared with Multiple Discriminate Analysis Model and neural network Models in Predicting Bankruptcy of the listed Companies in Tehran Stock Exchange

The main purpose of this paper is prediction of corporate financial bankruptcy using Artificial Neural Networks 1380-1389. The mean values of key ratios reported in past bankruptcy studies were selected for neural network inputs (Working capital to total assets, Net income to total assets, Total debt to total assets, Curent assets to current liabilities, Quick assets to current liabilities). The neural network used in this research is Multilayer Perceptron (MLP) that trained with backpropagation algorithm, and contained three-layer feedforward neural network with 5,18,2 number of neurons in input, hidden and output layer respectively. The samples of this research consist of bankrupt and non-bankrupt groups. Bankruptcy group was Manufacturing Corporations that were included Article 141 of Mercantile law within 1380-1389 and non-bankruptcy group selected by random sampling. The data is analysed using a more traditional method of bankruptcy prediction, multivariate discriminant analysis. A comparison of the predictive abilities of both the neural network and the discriminant analysis method is presented. Also, accuracy prediction of neural network is presented by ROC curve.