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Stock Market Simulation Using Support Vector Machines

Michael Breitner (), Christian Dunis, Hans-Jörg Mettenheim, Christopher Neely, Georgios Sermpinis, Rafael Rosillo, Javier Giner and David De la Fuente

Journal of Forecasting, 2014, vol. 33, issue 6, 488-500

Abstract: ABSTRACT The aim of this research was to analyse the different results that can be achieved using support vector machines (SVM) to forecast the weekly change movement of different simulated markets. The markets are developed by a GARCH model based on the S&P 500. These simulated markets are grouped by a main parameter: high volatility, bearish trend, bullish trend and low volatility. The inputs retained of the SVM are traditional technical trading rules used in quantitative analysis, such as relative strength index (RSI) and moving average convergence divergence (MACD) decision rules. The outputs of the SVM are the degree of set membership and market movement (bullish or bearish). The design of the SVM algorithm has been developed by Matlab and SVM‐KM. The configuration for the SVM shows that the best results are achieved in simulated markets with high volatility; also results are good in trend simulated markets. Copyright © 2014 John Wiley & Sons, Ltd.

Date: 2014
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