The Predictability Power of Neural Network and Genetic Algorithm from Companies’ Financial Crisis

Organizations expose to financial risk that can lead to bankruptcy and loss of business is increased nowadays. this may leads to discontinuity in operations, increased legal fees, administrative costs and other indirect costs. accordingly, the purpose of this study is to predict the financial crisis of tehran stock exchange using neural network and genetic algorithm. this research is descriptive and practical and in order to collect data stock exchange database software has been used. for data analysis, artificial neural network in base form and artificial neural network mix with genetic algorithm are used. in addition, for methods comparison, determination coefficient, mean squared error and root-mean square error have been used. the result of study shows that the best artificial neural network is a network with a hidden layer and eight neurons in the layer. this network could predict 97.7 percent of healthy and bankrupt companies correctly for test data. furthermore, the best-mixed neural network with genetic algorithm is a network with 400 replications and population size 50, one layer and eight neurons, which could correctly predict 100% of healthy and bankrupt companies. finally, comparison of results of two methods shows that the best method for predicting financial crisis is mixed neural network with genetic algorithm.