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A Sustainable Quantitative Stock Selection Strategy Based on Dynamic Factor Adjustment

Author

Listed:
  • Yi Fu

    (School of Finance and Business, Shanghai Normal University, Shanghai 200234, China)

  • Shuai Cao

    (School of Finance and Business, Shanghai Normal University, Shanghai 200234, China)

  • Tao Pang

    (Department of Mathematics, North Carolina State University, Raleigh, NC 27695-8205, USA)

Abstract

In this paper, we consider a sustainable quantitative stock selection strategy using some machine learning techniques. In particular, we use a random forest model to dynamically select factors for the training set in each period to ensure that the factors that can be selected in each period are the optimal factors in the current period. At the same time, the classification probability prediction (CPP) of stock returns is performed. Historical back-testing using Chinese stock market data shows that the proposed CPP quantitative stock selection strategy performs better than the traditional machine learning stock selection methods, and it can outperform the market index over the same period in most back-testing periods. Moreover, this strategy is sustainable in all market conditions, such as a bull market, a bear market, or a volatile market.

Suggested Citation

  • Yi Fu & Shuai Cao & Tao Pang, 2020. "A Sustainable Quantitative Stock Selection Strategy Based on Dynamic Factor Adjustment," Sustainability, MDPI, vol. 12(10), pages 1-12, May.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:10:p:3978-:d:357318
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    References listed on IDEAS

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    1. Shun Chen & Lei Ge, 2019. "Exploring the attention mechanism in LSTM-based Hong Kong stock price movement prediction," Quantitative Finance, Taylor & Francis Journals, vol. 19(9), pages 1507-1515, September.
    2. Fama, Eugene F. & French, Kenneth R., 1989. "Business conditions and expected returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 25(1), pages 23-49, November.
    3. Lin, Yi & Jeon, Yongho, 2006. "Random Forests and Adaptive Nearest Neighbors," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 578-590, June.
    4. Song, Frank M, 1994. "A Two-Factor ARCH Model for Deposit-Institution Stock Returns," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 26(2), pages 323-340, May.
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    Cited by:

    1. Caparrini, Antonio & Arroyo, Javier & Escayola Mansilla, Jordi, 2024. "S&P 500 stock selection using machine learning classifiers: A look into the changing role of factors," Research in International Business and Finance, Elsevier, vol. 70(PA).

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