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Stock Market Simulation Using Support Vector Machines

Author

Listed:
  • Michael H. Breitner
  • Christian Dunis
  • Hans-Jörg Mettenheim
  • Christopher Neely
  • Georgios Sermpinis
  • Rafael Rosillo
  • Javier Giner
  • David De la Fuente

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.

Suggested Citation

  • Michael H. Breitner & Christian Dunis & Hans-Jörg Mettenheim & Christopher Neely & Georgios Sermpinis & Rafael Rosillo & Javier Giner & David De la Fuente, 2014. "Stock Market Simulation Using Support Vector Machines," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(6), pages 488-500, September.
    • Handle: RePEc:wly:jforec:v:33:y:2014:i:6:p:488-500
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    Cited by:

    1. Aleesha Mohamudally-Boolaky & Teemulsingh Luchowa & Kesseven Padachi, 2019. "Applying the Support Vector Machine for Testing Pricing Inefficiency on the Stock Exchange of Mauritius," Applied Economics and Finance, Redfame publishing, vol. 6(5), pages 177-192, September.
    2. Dhruhi Sheth & Manan Shah, 2023. "Predicting stock market using machine learning: best and accurate way to know future stock prices," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 1-18, February.
    3. Zhou, Zhongbao & Gao, Meng & Liu, Qing & Xiao, Helu, 2020. "Forecasting stock price movements with multiple data sources: Evidence from stock market in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    4. Mostafaei, Kamran & maleki, Shaho & Zamani Ahmad Mahmoudi, Mohammad & Knez, Dariusz, 2022. "Risk management prediction of mining and industrial projects by support vector machine," Resources Policy, Elsevier, vol. 78(C).
    5. Hao Sun & Bo Yu, 2020. "Forecasting Financial Returns Volatility: A GARCH-SVR Model," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 451-471, February.
    6. Martín García, Rodrigo & Ventura Pérez, Enrique & Arguedas Sanz, Raquel, 2020. "Temporal optimisation of signals emitted automatically by securities exchange indicators," Cuadernos de Gestión, Universidad del País Vasco - Instituto de Economía Aplicada a la Empresa (IEAE).
    7. Zhang, Yongjie & Chu, Gang & Shen, Dehua, 2021. "The role of investor attention in predicting stock prices: The long short-term memory networks perspective," Finance Research Letters, Elsevier, vol. 38(C).
    8. Charalampos Stasinakis & Georgios Sermpinis & Ioannis Psaradellis & Thanos Verousis, 2016. "Krill-Herd Support Vector Regression and heterogeneous autoregressive leverage: evidence from forecasting and trading commodities," Quantitative Finance, Taylor & Francis Journals, vol. 16(12), pages 1901-1915, December.
    9. Sermpinis, Georgios & Stasinakis, Charalampos & Rosillo, Rafael & de la Fuente, David, 2017. "European Exchange Trading Funds Trading with Locally Weighted Support Vector Regression," European Journal of Operational Research, Elsevier, vol. 258(1), pages 372-384.

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