The Prediction of the Risk of Financial Bankruptcy Using Hybrid Model in Tehran Stock Exchange

Abstract

Predicting the risk of financial bankruptcy is one of the most important issues in the field of companies’ financial decision. Accordingly, a variety of models that each is different in terms of predictor variables and techniques has been introduced so far. The use of the combination of accounting and market-driven variables in the model as input will have definitely a direct impact on the results and accuracy of forecasts. In this study, the prediction was accomplished by using a hybrid model (the use of accounting and market-driven variables) and neural networks technique of multi-layer perceptron model (MLP). The sample of research consists of 90 accepted companies in Tehran Stock Exchange (31 bankrupted companies in accordance with article Iran’s 141 trade laws and 59 non-bankrupted companies) during 2007-2014 period. The research results show that the hybrid model (combination of accounting and market-driven variables) using neural network technique has higher accuracy than each of the two accounting models and market-driven model in predicting the risk of financial bankruptcy. Likewise, the market-driven model is more accurate than accounting model.

Keywords

• Risk of Financial Bankruptcy
• Accounting Variables
• Market-Driven Variables
• Hybrid Model
• Neural Network