Forecasting Stock Market Indices Using RVC-SVR

  • Jing-Xuan Huang
  • Jui-Chung Hung
Part of the Studies in Computational Intelligence book series (SCI, volume 551)

Abstract

This paper addresses stock market forecasting indices. Generally, the stock market index exhibits clustering properties and irregular fluctuation. This paper presents the results of using real volatility clustering (RVC) to analyze the clustering in support vector regression (SVR), called “real volatility clustering of support vector regression” (RVC-SVR). Combining RVC and SVR causes the parameters of estimation to become more difficult to solve, thus constituting a highly nonlinear optimization problem accompanied by many local optima. Thus, the genetic algorithm (GA) is used to estimate parameters.

Data from the Taiwan stock weighted index (Taiwan), Hang Seng index (Hong Kong), and NASDAQ (USA) were used as the simulation presented in this paper. Based on the simulation results, the stock indices forecasting accuracy performance is significantly improved when the SVR model considers the RVC.

Keywords

Support vector regression Forecasting index of stock market Genetic algorithm Real volatility clustering 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jing-Xuan Huang
    • 1
  • Jui-Chung Hung
    • 1
  1. 1.Department of Computer ScienceUniversity of TaipeiTaipeiTaiwan

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