Forecasting stock market indices using RVC-SVR

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.