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

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  • Zahedi, Javad
  • Rounaghi, Mohammad Mahdi

Abstract

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.

Suggested Citation

  • Zahedi, Javad & Rounaghi, Mohammad Mahdi, 2015. "Application of artificial neural network models and principal component analysis method in predicting stock prices on Tehran Stock Exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 178-187.
  • 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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    References listed on IDEAS

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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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    Cited by:

    1. Rounaghi, Mohammad Mahdi & Nassir Zadeh, Farzaneh, 2016. "Investigation of market efficiency and Financial Stability between S&P 500 and London Stock Exchange: Monthly and yearly Forecasting of Time Series Stock Returns using ARMA model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 10-21.

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