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Multi-factor Stock Selection Model Based on Adaboost

Author

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  • Ru Zhang
  • Tong Cao

Abstract

In this paper, we established multi-factor stock selection model based on Adaboost by using Adaboost to integrate the custom week classifier model, and Shanghai and Shenzhen 300 stocks are taken as the research object. During the stock retest, the first is make a comparative test between Adaboost multi-factor stock selection model and the traditional multi-factor model, among them, the factor large class isn¡¯t considered in the multi-factor stock selection model. And the results of two contrast experiment showed that the multi-factor stock selection model based on Adaboost has stronger profitability and less risk than the traditional multi-factor model.

Suggested Citation

  • Ru Zhang & Tong Cao, 2018. "Multi-factor Stock Selection Model Based on Adaboost," Business and Economic Research, Macrothink Institute, vol. 8(4), pages 119-129, December.
    • Handle: RePEc:mth:ber888:v:8:y:2018:i:4:p:119-129
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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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    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

    Quantitative investment; Multi-factor stock selection model; Adaboost;
    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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