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Multi-factor Stock Selection Model Based on Kernel Support Vector Machine

Author

Listed:
  • Ru Zhang
  • Zi-ang Lin
  • Shaozhen Chen
  • Zhixuan Lin
  • Xingwei Liang

Abstract

In recent years, the combination of machine learning method and traditional financial investment field has become a hotspot in academic and industry. This paper takes CSI 300 and CSI 500 stocks as the research objects. First, this paper carries out kernel function test and parameter optimization for the kernel support vector machine system, and then predict and optimize the combination of market-neutral stock selection strategy and stock right strategy. The results of the experiment show that the multi-factor model based on SVM has a strong predictive power for the selection of stock, and it has a difference in the predictive power of different nuclear functions.

Suggested Citation

  • Ru Zhang & Zi-ang Lin & Shaozhen Chen & Zhixuan Lin & Xingwei Liang, 2018. "Multi-factor Stock Selection Model Based on Kernel Support Vector Machine," Journal of Mathematics Research, Canadian Center of Science and Education, vol. 10(5), pages 9-18, October.
    • Handle: RePEc:ibn:jmrjnl:v:10:y:2018:i:5:p:9
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    References listed on IDEAS

    as
    1. Chen, Nai-fu & Zhang, Feng, 1998. "Risk and Return of Value Stocks," The Journal of Business, University of Chicago Press, vol. 71(4), pages 501-535, October.
    2. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
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    Citations

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    Cited by:

    1. Ganggang Guo & Yulei Rao & Feida Zhu & Fang Xu, 2020. "Innovative deep matching algorithm for stock portfolio selection using deep stock profiles," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-31, November.
    2. Shuang Zhang & Xingdong Feng, 2022. "Distributed identification of heterogeneous treatment effects," Computational Statistics, Springer, vol. 37(1), pages 57-89, March.
    3. Zeynep Cipiloglu Yildiz & Selim Baha Yildiz, 2022. "A portfolio construction framework using LSTM‐based stock markets forecasting," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 2356-2366, April.
    4. Jujie Wang & Zhenzhen Zhuang & Liu Feng, 2022. "Intelligent Optimization Based Multi-Factor Deep Learning Stock Selection Model and Quantitative Trading Strategy," Mathematics, MDPI, vol. 10(4), pages 1-19, February.

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    More about this item

    Keywords

    multi-factor model; support vector machine; quantitative investment;
    All these keywords.

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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