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Predicting Stock Prices Moving Trend with Support Vector Machine Based on Ant Colony Optimization in Tehran Stock Exchange

Nazanin Pouria

Abstract of Economic, Finance and Management Outlook, 2016, vol. 6, 6

Abstract: Predicting and surveying the behavior of stocks is the subject which financial experts and investors pursue all the time. The main reason to invest in the stock market is to gain benefit whose prerequisites are correct information about the stock market as well as stocks fluctuations and predicting the future tendencies; therefore, the investors require powerful and reliable tools, through which they can predict the price of stocks. As a result, this study tries to present a model based on which the trend of the stocks could be predicted. According to the aforementioned facts, a hybrid model to predict stocks prices trend is presented via a Support Vector Machine based on Ant Colony Algorithm. For this aim, 24 Iranian companies out of the top 50 ones in the third trimester of 2014 as well as 6 ones out of the top 50 in the fourth trimester of 2014 were chosen for statistical samples. Afterwards, 51 technical indicator variables were calculated for all the 30 companies. The supporting variables are the input for the hybrid model and are optimized taking advantage of Ant Colony Algorithm. The given results show that Support Vector Machine based on Ant colony optimization has better performance in predicting stocks prices trends; since the true forecast average showed higher marks, the model owned higher accuracy in predicting the stocks prices trend. Key Words: Prediction, Stocks Prices Trend, Support Vector Machine, Ant Colony Optimization Algorithm, Technical Analysis, Technical Indicator

Date: 2016
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