predicting stock price movement using support vector machine based on genetic algorithm in tehran stock exchange market

according to recent developments of predicting methodsin financial markets, and since the stock price is one of the mostimportant factors for investment decision-making, and its predictioncan play an important role in this field, the aim of this study is toprovide a model to predict the stock price movement with highaccuracy. accordingly, a hybrid model for predicting the stock pricemovement using support vector machine (svm) based on geneticalgorithms is presented. thirty companies from the top 50 companiesin tehran stock exchange in 2011 are selected as sample. then, foreach company, 44 variables have been calculated. these variables arethe inputs of the hybrid model and are optimized using geneticalgorithm. the results show that the hybrid model of support vectormachine based on genetic algorithms has better performance inpredicting the stock price movement and it has a higher accuracycompared with the simple support vector machine.