A hybrid model based on set theory and genetic algorithm to predict the stock price
Journal Title: International Research Journal of Applied and Basic Sciences - Year 2013, Vol 7, Issue 12
Abstract
Predicting the future is always a necessity in daily life and as a member of the science has been around for many years. One area in which the forecast is of great importance, issues of economic and financial market exchange is indeed. The most important issues for investors in stock market is forecast stock price. In this study, an overview and then explore methods to predict the non-linear nature of price behavior in the stock market, the model theory of genetic algorithm to predict the stock price, were used.Population and Sample The study sample includes firms listed in Tehran Stock Exchange (automotive industry) stands for the years 2002 to 2012 have been active in the stock. The sample of 10 companies is from the automotive companies, which have the highest trading volume of over 10 years, is. Then, after designing, implementing models of genetic theory of rhythm patterns and a hybrid model using a measure of error (MSE) results of the three models were compared. The results show that the genetic algorithm model forecasts better than the theory of sets Hybrid model forecasts better than the previous model had And using technical analysis indicators as input models have a large impact on the estimation error.
Authors and Affiliations
Kamal Zareimoravej| Department of Accounting, Hamedan Branch, Islamic Azad University, Hamedan, Iran, email: zareie.iaub@yahoo.com, Ahmad Heidari| Academic member, Farhanghian University, Tehran, I.R.of Iran, Abas Zarei| Department of Accounting, fani herfei University, Hamedan , Iran, Jafar Zarin| Department of Accounting, Payam Noor University, Tehran, Iran
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