Modeling of Stock Price Forecasting in Stock Exchange Market, using Fuzzy Neural Networks and Genetic Algorithms

Investment in common stocks is one of most beneficent alternatives in capital markets, therefore forecasting of stock price is great significance for shareholders; however the complexity of stock markets. Most studies revealed stock price in Tehran stock exchange has not random walk pattern. Besides, with regard to nonlinear and chaos system in stock market that eclipsed of political, economical and psychological conditions, we can use nonlinear artificial systems such as artificial neural networks (ANN), fuzzy neural networks (FNN) and genetic algorithms (GA’s) for forecasting stock price. Two goals are aimed in this research: 1- Design and present a model for forecasting stock price, using FNN and GA (GFNN). 2- Reduction the forecasting error of GFNN stock price forecasting model, in compare with mere ANN. At first, for examination of mentioned goals, multi layer perceptron (MLP) of ten listing companies in Tehran stock exchange has been designed. Then, after design and implementation of GFNN, the results of two models, have been compared and hypotheses have been investigated by using error evaluating criteria (MSE, R2, NMSE and MAPE). The results of research show, in compare with mere ANN, GFNN has perfect predictions and has higher speed and more strong estimation for stock price forecasting.