Investigating Financial Crisis Prediction Power using Neural Network and Non-Linear Genetic Algorithm

Bankruptcy is an event with strong impacts on management, shareholders, employees, creditors, customers and other stakeholders, so as bankruptcy challenges the country both socially and economically. Therefore, correct prediction of bankruptcy is of high importance in the financial world. This research intends to investigate financial crisis prediction power using models based on Neural Networks and to compare it with Non-Linear Genetic Algorithm. Based on the available information and statistics of the listed companies on Tehran Stock Exchange (TSE) during 1997-2010, from among these companies subjected to article 141 of the Commercial Law, 72 firms, and from among other firms, 72 firms were selected.    Results of McNemar Test for Non-Linear Genetic Algorithm and Neural Network indicated that although prediction accuracy of Non-Linear Genetic Algorithm (90%) was greater than that of Neural Network (70%), yet this difference was not statistically significant