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

Fallahpour, Saeeid; Golarzi, Gholamhossein; Fatourechian, Naser

doi:10.22059/jfr.2013.51081

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

Document Type : Research Paper

Authors

Saeeid Fallahpour ¹

Gholamhossein Golarzi ²

Naser Fatourechian ³

¹ Assistant Prof. Finance Management, University of Tehran, Iran

² Assistant Prof. Finance Management, University of Semnan, Semnan, Iran

³ MSc. MBA-Finance, University of Semnan, Semnan, Iran

10.22059/jfr.2013.51081

Abstract

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 companies
in 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.

Keywords

Genetic Algorithm

Predicting

Support vector Machine

Stock Price

Technical Analysis