Abstract: This paper develops an efficient forecasting model for various stock price indices based on the previously introduced particle swarm optimization with center mass (PSOCOM) technique. The structure used in the proposed prediction models is a simple linear combiner using (PSOCOM) by minimizing its mean square error (MSE) to evaluate the proposed model. The comparison with other models such as standard PSO, Genetic algorithm, Bacterial foraging optimization, and adaptive bacterial foraging optimization had been done. The experimental results show that PSOCOM algorithms are the best among other algorithms in terms of MSE and the accuracy of prediction for some stock price indices. Whereas, the proposed forecasting model gives accurate prediction for short- and long-term prediction. As a result, the proposed stock market prediction model is more efficient from the other compared models.

Razan A. Jamous, Essam El.Seidy and Bayoumi Ibrahim Bayoum, “A Novel Efficient Forecasting of Stock Market Using Particle Swarm Optimization with Center of Mass Based Technique” International Journal of Advanced Computer Science and Applications(IJACSA), 7(4), 2016. http://dx.doi.org/10.14569/IJACSA.2016.070445

@article{Jamous2016,
title = {A Novel Efficient Forecasting of Stock Market Using Particle Swarm Optimization with Center of Mass Based Technique},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.070445},
url = {http://dx.doi.org/10.14569/IJACSA.2016.070445},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {4},
author = {Razan A. Jamous and Essam El.Seidy and Bayoumi Ibrahim Bayoum}
}