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A Stock Market Prediction Method Based on Support Vector Machines (SVM) and Independent Component Analysis (ICA)

Author

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  • Hakob GRIGORYAN

    (University of Economic Studies, Bucharest, Romania)

Abstract

The research presented in this work focuses on financial time series prediction problem. The integrated prediction model based on support vector machines (SVM) with independent component analysis (ICA) (called SVM-ICA) is proposed for stock market prediction. The presented approach first uses ICA technique to extract important features from the research data, and then applies SVM technique to perform time series prediction. The results obtained from the SVM-ICA technique are compared with the results of SVM-based model without using any pre-processing step. In order to show the effectiveness of the proposed methodology, two different research data are used as illustrative examples. In experiments, the root mean square error (RMSE) measure is used to evaluate the performance of proposed models. The comparative analysis leads to the conclusion that the proposed SVM-ICA model outperforms the simple SVM-based model in forecasting task of nonstationary time series.

Suggested Citation

  • Hakob GRIGORYAN, 2016. "A Stock Market Prediction Method Based on Support Vector Machines (SVM) and Independent Component Analysis (ICA)," Database Systems Journal, Academy of Economic Studies - Bucharest, Romania, vol. 7(1), pages 12-21, August.
    • Handle: RePEc:aes:dbjour:v:7:y:2016:i:1:p:12-21
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    References listed on IDEAS

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    1. Tim Hill & Marcus O'Connor & William Remus, 1996. "Neural Network Models for Time Series Forecasts," Management Science, INFORMS, vol. 42(7), pages 1082-1092, July.
    2. Catalina Lucia COCIANU & Hakob GRIGORYAN, 2015. "An Artificial Neural Network for Data Forecasting Purposes," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 19(2), pages 34-45.
    3. Tay, Francis E. H. & Cao, Lijuan, 2001. "Application of support vector machines in financial time series forecasting," Omega, Elsevier, vol. 29(4), pages 309-317, August.
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    Cited by:

    1. 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.
    2. Liu, Keyan & Zhou, Jianan & Dong, Dayong, 2021. "Improving stock price prediction using the long short-term memory model combined with online social networks," Journal of Behavioral and Experimental Finance, Elsevier, vol. 30(C).
    3. Hüseyin İlker Erçen & Hüseyin Özdeşer & Turgut Türsoy, 2022. "The Impact of Macroeconomic Sustainability on Exchange Rate: Hybrid Machine-Learning Approach," Sustainability, MDPI, vol. 14(9), pages 1-19, April.
    4. Shuheng Wang & Guohao Li & Yifan Bao, 2018. "A novel improved fuzzy support vector machine based stock price trend forecast model," Papers 1801.00681, arXiv.org.
    5. Akshit Kurani & Pavan Doshi & Aarya Vakharia & Manan Shah, 2023. "A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting," Annals of Data Science, Springer, vol. 10(1), pages 183-208, February.

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