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

• Main Authors: Saeeid Fallahpour, Gholamhossein Golarzi, Naser Fatourechian
• Format: Article
• Language: fas
• Published: University of Tehran 2013-10-01
• Series: تحقیقات مالی
• Subjects: genetic algorithm; predicting; support vector machine; stock price; technical analysis
• Online Access: https://jfr.ut.ac.ir/article_51081_7b62bf20041a4c2bd73a890dc8a3a0ec.pdf

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