Stock Based On SSA And SVM

Topics: Support vector machine, Stock market, Singular value decomposition Pages: 7 (2251 words) Published: January 14, 2015
Available online at www.sciencedirect.com

ScienceDirect
Procedia Computer Science 31 (2014) 625 – 631

2nd International Conference on Information Technology and Quantitative Management, ITQM 2014

Stock Price Prediction Based on SSA and SVM
WEN Fenghuaa*, XIAO Jihongb, HE Zhifanga, GONG Xua
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. ©

The Authors.
Published
Elsevier
© 2014
2014 Published
by Elsevier
B.V.byOpen
accessB.V.
under CC BY-NC-ND license.
Selection
and peer-review
peer-reviewunder
underresponsibility
responsibility
of the
Organizing
Committee
of ITQM
Selection and
of the
Organizing
Committee
of ITQM
2014. 2014.
Keywords: Stock Price Series; Singular Spectrum Analysis; Support Vector Machine (SVM); Combination Predictive Methods

1. Introduction
Recently, the support vector machine2 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 al4 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 King7. 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.

1877-0509 © 2014 Published by Elsevier B.V. Open access under CC BY-NC-ND license.

Selection and peer-review under responsibility of the Organizing Committee of ITQM 2014. doi:10.1016/j.procs.2014.05.309

626

Fenghua Wen et al. / Procedia Computer Science 31 (2014) 625 – 631

can derive time series with different features, and they are often used for extracting noise in time series. Beneki, et al8 extracted the trend term and economic fluctuation term, and put them into the analyses of tourist revenues series in Britain; Chinese scholar Lian Jijian, et al9 used order determination and noise reduction based on singular entropy to reduce noise in series. In this paper, we also use the SSA to decompose the stock price. As mentioned above, to make predictions more accurate, we use the SSA to decompose stock price series into the terms of trend, market fluctuation and noise, and then introduce these terms into the SVM to make price predictions.

2. Introduction to Predictive Methods
2.1 SSA
The core idea of the SSA is to obtain a series of singular values which contain the information of the original series through the singular spectral decomposition (SVD), and then select different singular values to construct series with...
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