The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Metadata Harvesting (OAI2)
  • Digital Archiving Policy
  • Promote your Publication

IJACSA

  • About the Journal
  • Call for Papers
  • Author Guidelines
  • Fees/ APC
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Editors
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors

Future of Information and Communication Conference (FICC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Guidelines
  • Fees
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Editors
  • Reviewers
  • Subscribe

Article Details

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.

A Novel Efficient Forecasting of Stock Market Using Particle Swarm Optimization with Center of Mass Based Technique

Author 1: Razan A. Jamous
Author 2: Essam El.Seidy
Author 3: Bayoumi Ibrahim Bayoum

Download PDF

Digital Object Identifier (DOI) : 10.14569/IJACSA.2016.070445

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

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

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.

Keywords: Stock market forecasting; particle swarm optimization; Bacterial foraging optimization; Adaptive bacterial foraging optimization; Genetic algorithm

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}
}


IJACSA

Upcoming Conferences

Future of Information and Communication Conference (FICC) 2023

2-3 March 2023

  • Virtual

Computing Conference 2023

22-23 June 2023

  • London, United Kingdom

IntelliSys 2023

7-8 September 2023

  • Amsterdam, The Netherlands

Future Technologies Conference (FTC) 2023

2-3 November 2023

  • San Francisco, United States
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. Registered in England and Wales. Company Number 8933205. All rights reserved. thesai.org