A New Approach for Stock Price Analysis and Prediction Based on SSA and SVM

Jihong Xiao, Xuehong Zhu, Chuangxia Huang (), Xiaoguang Yang (), Fenghua Wen and Meirui Zhong
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Jihong Xiao: School of Business, Central South University, Changsha 410083, P. R. China†Institute of Metal Resources Strategy, Changsha 410083, P. R. China
Xuehong Zhu: School of Business, Central South University, Changsha 410083, P. R. China†Institute of Metal Resources Strategy, Changsha 410083, P. R. China
Chuangxia Huang: #x2021;College of Mathematics and Computing Science, Changsha University of Science and Technology, Changsha 410004, P. R. China
Xiaoguang Yang: #xA7;Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, P. R. China
Fenghua Wen: School of Business, Central South University, Changsha 410083, P. R. China¶Supply Chain and Logistics Optimization Research Centre, Faculty of Engineering, University of Windsor, Windsor, ON, Canada∥Centre for Computational Finance and Economic Agents, University of Essex, Colchester CO4 3SQ, UK
Meirui Zhong: School of Business, Central South University, Changsha 410083, P. R. China†Institute of Metal Resources Strategy, Changsha 410083, P. R. China

International Journal of Information Technology & Decision Making (IJITDM), 2019, vol. 18, issue 01, 287-310

Abstract: Stock price exhibits distinct features during different time scales due to the effects of complex factors. Analyzing these features can help delineate the mechanisms that determine the stock price and enhance the prediction accuracy of the stock price. By using singular spectrum analysis (SSA), this paper first decomposes the original price series into a trend component, a market fluctuation component and a noise component to analyze the stock price. The economic meanings of the three components are identified as a long-term trend, effects of significant events and short-term fluctuations caused by noise in the market. Then, to take into account the features of the above three components to the stock price prediction, a novel combined model that integrates SSA and support vector machine (SVM) (e.g., SSA–SVM) is proposed. Compared with SVM, adaptive network-based fuzzy inference system (ANFIS), ensemble empirical mode decomposition-ANFIS (EEMD–ANFIS), EEMD–SVM and SSA–ANFIS, SSA–SVM demonstrates the best prediction performance based on four criteria, indicating that the proposed model is a promising approach for stock price prediction.

Keywords: Stock price; singular spectrum analysis; support vector machine; combined model (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (4)

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DOI: 10.1142/S021962201841002X

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