Developing an Optimal Method for Financial Distress Prediction of the Firms (Case Study: Tehran Stock Exchange)
Subject Areas :
Journal of Investment Knowledge
Mansour Soufi
1
,
Mahdi Homayounfar
2
,
Mehdi Fadaei
3
1 - Assistant professor in Department of Industrial Management, Rasht Branch, Islamic Azad University, Rasht, Iran
2 - Assistant professor in Department of Industrial Management, Rasht Branch, Islamic Azad University, Rasht, Iran
3 - Assistant professor in Department of Industrial Management, Rasht Branch, Islamic Azad University, Rasht, Iran
Received: 2018-09-01
Accepted : 2018-10-20
Published : 2020-11-21
Keywords:
Forecasting,
Artificial Neural Network,
Genetic algorithm,
Financial Distress,
Abstract :
One of the most important issues in the field of financial management is how the investors distinguish between favorable investment opportunities and undesirable ones. One of the ways to help investors is to provide financial distress prediction models. According to the various studies have been made to develop these type of models, in this study the combination of artificial neural networks (ANN) and genetic algorithm (GA) techniques based on Zimensky prediction ratios is used for modeling financial distress. The research statistical population includes public companies in Tehran stock exchange which admitted between October 2013 to October 2015 and among them 66 distressed and 150 going concern companies were selected as the research sample using screening method. The results indicate that the power of both artificial neural network and genetic algorithm models in financial distress prediction are equal (95 percent), however, the prediction error of neural network is relatively low compared to genetic algorithm.
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