The Comparison of Financial Crisis Prediction Strength of Different Artificial Intelligence Techniques

Subject Areas :

Zahra Pourzamani ¹ , Hassan kalantari ²

1 - استادیار دانشگاه آزاد اسلامی واحد تهران مرکزی
2 - کارشناس ارشد حسابداری دانشگاه آزاد اسلامی واحد تهران مرکزی

Received: 2015-05-08 Accepted : 2015-05-08 Published : 2013-05-22

Keywords: Bankruptcy Prediction, Linear Genetic Algorithms, Nonlinear Genetic Algorithm, Neural network,

Abstract :

Rapid technological advances and vast environmental changes, leading to increasing competition and limit access to benefits and likely to suffer financial crisis has increased. Purpose of this study is investigating financial crisis prediction strength of different artificial intelligence techniques(linear and nonlinear genetic algorithm and neural network). Based on available information and statistics, of all companies listed in Tehran Stock Exchange, 72 companies have been subject to Article 141 trade law and 72 companies have not been subject to this Article was elected. Results of Mc-Nemar test for genetic algorithms techniques and neural network showed that there are not significant differences between linear and nonlinear genetic algorithms with neural network. Although the predictive accuracy of nonlinear genetic algorithm(90%) and linear genetic algorithms(80%) is more than of the neural network(70%) but this difference is not statistically significant.

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