forecasting stock market using wavelet transforms and neural networks: an integrated system based on fuzzy genetic algorithm (case study of price index of tehran stock exchange)

the jamor purpose of the present research is to predict the total stock market index of tehran stock exchange, using a combined method of wavelet transforms, fuzzy genetics, and neural network in order to predict the active participations of finance market as well as macro decision makers.to do so, first the prediction was made by neural network, then a series of price index was decomposed by wavelet transform and the prediction made by neural network was repeated, finally, the extracted pattern from the neural network was stated through discernible rules using fuzzy theory. the main focus of this paper is based on a theory in which investors and traders achieve a method for predicting stock market. concerning the results of previous researches, which confirmed the relative superiority of non-linear models in price index prediction, an appropriate model has been offered in this research by combining the non-linear methods such as wavelet transforms, fuzzy genetics, and neural network, the results indicated the superiority of the designed system in predicting price index of tehran stock exchange.