Procedia Computer Science 31 (2014) 625 – 631 Available online at www.sciencedirect.com 1877-0509 © 2014 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). Selection and peer-review under responsibility of the Organizing Committee of ITQM 2014. doi:10.1016/j.procs.2014.05.309 ScienceDirect 2nd International Conference on Information Technology and Quantitative Management, ITQM 2014 Stock Price Prediction Based on SSA and SVM WEN Fenghua a* , XIAO Jihong b , HE Zhifang a , GONG Xu a a. Business School of Central South University, Changsha, 410081, China b. School of Economics and Management, Changsha University of Science and Technology, Changsha, 410004 ,China Abstract This paper, using the singular spectrum analysis (SSA), decomposes the stock price into terms of the trend, the market fluctuation, and the noise with different economic features over different time horizons, and then introduce these features into the support vector machine (SVM) to make price predictions. The empirical evidence shows that, compared with the SVM without these price features, the combination predictive methods——the EEMD-SVM and the SSA-SVM, which combine the price features into the SVMs perform better, with the best prediction to the SSA-SVM. © 2014 The Authors. Published by Elsevier B.V. Selection and peer-review under responsibility of the Organizing Committee of ITQM 2014. Keywords: Stock Price Series; Singular Spectrum Analysis; Support Vector Machine (SVM); Combination Predictive Methods 1. Introduction Recently, the support vector machine 2 has been widely used in stock price predictions. However, as the stock market is affected by economic, political, financial, social factors and noises, stock prices may have different features over different time horizons. But few studies have introduced the price features into the SVM to make price predictions. Zhang, et al 4 used the ensemble empirical mode decomposition (EEMD) to analyze fundamental features of petroleum price series over different time horizons and pointed out that the decomposed terms can be introduced into the SVM to make predictions. But the EEMD has some limitations in the analysis of stock price series. The EEMD can not effectively extract noise from the price prediction, but the impact of noise is prevalent in the stock market. Therefore, the EEMD can not catpure this feature well. The SSA, a method for analyzing non-linear, non-stationary time series, was first proposed by Broomhead and King 7 . The SSA is to get a series of singular values which contains the information of the original series through the singular spectral decomposition (SVD). By analyzing singular values of different information, we *Corresponding author: wfh@amss.ac.cn. © 2014 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). Selection and peer-review under responsibility of the Organizing Committee of ITQM 2014.