  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.
