—The goal of this research is to analyse the different results that can be achieved using Support Vector Machines to forecast the weekly change movement of the different simulated markets. The data cover 3000 daily close for each simulated market. The main characteristic of these markets are: high volatility, bearish movement, bullish movement and low volatility. The inputs of the SVM are the Relative Strength Index (RSI) and the Moving Average Convergence Divergence (MACD). SVM-KM is used by Matlab in order to design the algorithm. The outputs of the SVM are the degree of set membership and the market movement (bullish or bearish). The configuration for the SVM shows that results are better in high volatility markets or low volatility markets than trend markets.
—Support vector machines, quantitative trading, stock market models, technical analysis.
Rafael Rosillo, Javier Puente, and Borja Ponte are with the Polytechnic School of Engineering, University of Oviedo, Campus de Viesques s/n, CP 33204, Gijón, Asturias, Spain (e-mail: firstname.lastname@example.org, email@example.com, firstname.lastname@example.org). Javier Giner is with the Faculty of Economics and Business, University of La Laguna, Campus de Guajara s/n, CP 38071, La Laguna, Islas Canarias, Spain (e-mail: email@example.com).
Cite:Rafael Rosillo, Javier Giner, Javier Puente, and Borja Ponte, "Different Stock Market Models Using Support Vector Machines," International Journal of Trade, Economics and Finance vol.4, no.5, pp. 310-313, 2013.