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DOI: 10.14569/IJACSA.2016.070442
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A New Particle Swarm Optimization Based Stock Market Prediction Technique

Author 1: Essam El. Seidy

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 7 Issue 4, 2016.

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Abstract: Over the last years, the average person's interest in the stock market has grown dramatically. This demand has doubled with the advancement of technology that has opened in the International stock market, so that nowadays anybody can own stocks, and use many types of software to perform the aspired profit with minimum risk. Consequently, the analysis and prediction of future values and trends of the financial markets have got more attention, and due to large applications in different business transactions, stock market prediction has become a critical topic of research. In this paper, our earlier presented particle swarm optimization with center of mass technique (PSOCoM) is applied to the task of training an adaptive linear combiner to form a new stock market prediction model. This prediction model is used with some common indicators to maximize the return and minimize the risk for the stock market. The experimental results show that the proposed technique is superior than the other PSO based models according to the prediction accuracy.

Keywords: Computational intelligence; Particle Swarm Optimization; Stock Market; Prediction

Essam El. Seidy, “A New Particle Swarm Optimization Based Stock Market Prediction Technique” International Journal of Advanced Computer Science and Applications(IJACSA), 7(4), 2016. http://dx.doi.org/10.14569/IJACSA.2016.070442

@article{Seidy2016,
title = {A New Particle Swarm Optimization Based Stock Market Prediction Technique},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.070442},
url = {http://dx.doi.org/10.14569/IJACSA.2016.070442},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {4},
author = {Essam El. Seidy}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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