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Mathematical Problems in Engineering
Volume 2015, Article ID 231394, 7 pages
http://dx.doi.org/10.1155/2015/231394
Research Article

A Hybrid Least Square Support Vector Machine Model with Parameters Optimization for Stock Forecasting

1International Business School, Shaanxi Normal University, Xian 710062, China
2Department of Management Sciences, City University of Hong Kong, Hong Kong
3School of Business, Tung Wah College, Hong Kong

Received 30 May 2014; Accepted 20 August 2014

Academic Editor: Shifei Ding

Copyright © 2015 Jian Chai et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper proposes an EMD-LSSVM (empirical mode decomposition least squares support vector machine) model to analyze the CSI 300 index. A WD-LSSVM (wavelet denoising least squares support machine) is also proposed as a benchmark to compare with the performance of EMD-LSSVM. Since parameters selection is vital to the performance of the model, different optimization methods are used, including simplex, GS (grid search), PSO (particle swarm optimization), and GA (genetic algorithm). Experimental results show that the EMD-LSSVM model with GS algorithm outperforms other methods in predicting stock market movement direction.