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Application of artificial neural network models and principal component analysis method in predicting stock prices on Tehran Stock Exchange

Listed author(s):
  • Zahedi, Javad
  • Rounaghi, Mohammad Mahdi
Registered author(s):

    Stock price changes are receiving the increasing attention of investors, especially those who have long-term aims. The present study intends to assess the predictability of prices on Tehran Stock Exchange through the application of artificial neural network models and principal component analysis method and using 20 accounting variables. Finally, goodness of fit for principal component analysis has been determined through real values, and the effective factors in Tehran Stock Exchange prices have been accurately predicted and modeled in the form of a new pattern consisting of all variables.

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    Article provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.

    Volume (Year): 438 (2015)
    Issue (Month): C ()
    Pages: 178-187

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    Handle: RePEc:eee:phsmap:v:438:y:2015:i:c:p:178-187
    DOI: 10.1016/j.physa.2015.06.033
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    1. Koulouriotis, D.E. & Emiris, D.M. & Diakoulakis, I.E. & Zopounidis, C., 2002. "Behavioristic Analysis And Comparative Evaluation Of Intelligent Methodologies For Short-Term Stock Price Forecasting," Fuzzy Economic Review, International Association for Fuzzy-set Management and Economy (SIGEF), vol. 0(2), pages 23-57, November.
    2. Yi-Hsien Wang, 2009. "Using neural network to forecast stock index option price: a new hybrid GARCH approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 43(5), pages 833-843, September.
    3. Olson, Dennis & Mossman, Charles, 2003. "Neural network forecasts of Canadian stock returns using accounting ratios," International Journal of Forecasting, Elsevier, vol. 19(3), pages 453-465.
    4. Duan, Wen-Qi & Stanley, H. Eugene, 2011. "Cross-correlation and the predictability of financial return series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(2), pages 290-296.
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