Forecasting Stock Market Indices Using RVC-SVR

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Title:	Forecasting Stock Market Indices Using RVC-SVR
Authors:	Hung, Jui-Chung;洪瑞鍾;Huang, Jing-Xuan
Contributors:	臺北市立大學資訊科學系
Keywords:	Support vector regression;Forecasting index of stock market;Genetic algorithm;Real volatility clustering
Date:	2014
Issue Date:	2017-07-24 11:27:30 (UTC+8)
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.
Relation:	Advanced Approaches to Intelligent Information and Database Systems Studies in Computational Intelligence Volume 551, pp 89-96
Appears in Collections:	[Department of Computer Science] Proceedings

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