Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on a financial exchange. The successful prediction of a stock`s future price will maximize investor’s gains. In this paper we analyze a machine learning model to predict stock market price, where existing algorithm integrates Particle swarm optimization (PSO) and least square support vector machine (LS-SVM) are identified in which, the PSO algorithm is employed to optimize LS-SVM to predict the daily stock prices. The proposed model is based on the study of stocks historical data and technical indicators. PSO algorithm selects best free parameters combination for LS-SVM to avoid over-fitting and local minima problems and improve prediction accuracy. The proposed model was also applied and evaluated using thirteen benchmark financials datasets and compared with artificial neural network with Levenberg-Marquardt (LM) algorithm. The obtained results showed that the proposed model has better prediction accuracy and the potential of PSO algorithm in optimizing LS-SVM.

Least Square Support Vector Machine, Particle Swarm Optimization, Technical Indicators and Stock Price prediction.

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