LSSVM-ABC Algorithm for Stock Price prediction
 | International Journal of ComputerTrends and Technology (IJCTT) |  |
| © 2014 by IJCTT Journal | ||
| Volume-7 Number-2 | ||
| Year of Publication : 2014 | ||
| Authors : Osman Hegazy , Omar S. Soliman and Mustafa Abdul Salam | ||
| DOI : 10.14445/22312803/IJCTT-V7P121 |
Osman Hegazy , Omar S. Soliman and Mustafa Abdul Salam. Article: LSSVM-ABC Algorithm for Stock Price prediction. International Journal of Computer Trends and Technology (IJCTT) 7(2):81-92, January 2014. Published by Seventh Sense Research Group.
Abstract-
In this paper, Artificial Bee Colony (ABC) algorithm which inspired from the behavior of honey bees swarm is presented. ABC is a stochastic population-based evolutionary algorithm for problem solving. ABC algorithm, which is considered one of the most recently swarm intelligent techniques, is proposed to optimize least square support vector machine (LSSVM) to predict the daily stock prices. The proposed model is based on the study of stocks historical data, technical indicators and optimizing LSSVM with ABC algorithm. ABC selects best free parameters combination for LSSVM to avoid over-fitting and local minima problems and improve prediction accuracy. LSSVM optimized by Particle swarm optimization (PSO) algorithm, LSSVM, and ANN techniques are used for comparison with proposed model. Proposed model tested with twenty datasets representing different sectors in S&P 500 stock market. Results presented in this paper show that the proposed model has fast convergence speed, and it also achieves better accuracy than compared techniques in most cases.
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Keywords- Least square support vector machine, Artificial Bee Colony, technical indicators, and stock price prediction