Predicting Stock Price Movement Using Support Vector Machine Based on Genetic Algorithm in Tehran Stock Exchange Market

According to recent developments of predicting methods in financial markets، and since the stock price is one of the most important factors for investment decision-making، and its prediction can play an important role in this field، the aim of this study is to provide a model to predict the stock price movement with high accuracy. Accordingly، a hybrid model for predicting the stock price movement using Support Vector Machine (SVM) based on genetic algorithms is presented. Thirty companies from the top 50 companiesin Tehran Stock Exchange in 2011 are selected as sample. Then، for each company، 44 variables have been calculated. These variables are the inputs of the hybrid model and are optimized using genetic algorithm. The results show that the hybrid model of Support Vector Machine based on genetic algorithms has better performance in predicting the stock price movement and it has a higher accuracy compared with the simple Support Vector Machine.