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Index Prediction in Tehran Stock Exchange Using Hybrid Model of Artificial Neural Networks and Genetic Algorithms

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  • Farzad Karimi
  • Mohsen Dastgir
  • Monireh Shariati

    ()

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    Abstract

    Nowadays, investment in the bource organizes the important part of country economy. So the prediction of stocks index is very important for stockholders to earn the highest return from their investment. The changes of stock market influence by several factors such as political, economical and social factors and maybe, using the classic methods for stock market prediction result in exact results. Since, the intelligent method have this capability that consider the complex effects of above factors in the analysis, so we can use them in stock index prediction. Therefore, we can use the artificial neural network for predicting and the genetic algorithm for optimizing the input variable in the neural network. This research has studied the efficiency of compound model, artificial neural network and genetic algorithm for predicting the stock index of Tehran negotiable documents bourse. For this reason, the economical data such as open market price of foreign exchange, bubble of coin price, the price of each ounce of gold in international market and the price of OPEK oil basket as inputs of compound model and total index of bource that compared with outputs of compound model, and have collected from 23st of September in 2010 until 18th of March in 2013 daily. The results indicated that the compound model, artificial neural network and genetic algorithm, can accomplish the exact prediction and this model operate efficiently.

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    Bibliographic Info

    Article provided by Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences in its journal International Journal of Academic Research in Accounting, Finance and Management Sciences.

    Volume (Year): 4 (2014)
    Issue (Month): 1 (January)
    Pages: 352-357

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    Handle: RePEc:hur:ijaraf:v:4:y:2014:i:1:p:352-357

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    Web page: http://hrmars.com/index.php/pages/detail/Accounting-Finance-Journal

    Related research

    Keywords: Stocks index; artificial neural network; genetic algorithm;

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