Research article

Stock selection strategy of A-share market based on rotation effect and random forest

  • Received: 09 January 2020 Accepted: 30 April 2020 Published: 22 May 2020
  • MSC : 62-XX, 91-XX

  • Due to the random nature of stock market, it is extremely difficult to capture market trends with traditional subjective analysis. Besides, the modeling and forecasting of quantitative investment strategies are not easy. Based on the research of experts and scholars at home and abroad and the rotation effect of large and small styles in China's A-share market, a stock picking strategy combining wheeling effect and random forest is proposed. Firstly, judge the style trend of the A-share market, that is, the relatively strong style in the large and small-sized market. The strategy first judges the trend of the A-share market style, then uses a multi-factor stock selection model through random forest to select stocks among the constituent stocks of the dominant style index, and buys the selected stocks according to the optimal portfolio weights determined by the principle of minimum variance. The empirical results show that the annualized rate of return of the strategy in the eight years from January 1, 2012 to April 1, 2020 is 3.6% higher than that of the single-round strategy, far exceeding the performance of the CSI 300 Index during the same period.

    Citation: Shuai Wang, Zhongyan Li, Jinyun Zhu, Zhicen Lin, Meiru Zhong. Stock selection strategy of A-share market based on rotation effect and random forest[J]. AIMS Mathematics, 2020, 5(5): 4563-4580. doi: 10.3934/math.2020293

    Related Papers:

  • Due to the random nature of stock market, it is extremely difficult to capture market trends with traditional subjective analysis. Besides, the modeling and forecasting of quantitative investment strategies are not easy. Based on the research of experts and scholars at home and abroad and the rotation effect of large and small styles in China's A-share market, a stock picking strategy combining wheeling effect and random forest is proposed. Firstly, judge the style trend of the A-share market, that is, the relatively strong style in the large and small-sized market. The strategy first judges the trend of the A-share market style, then uses a multi-factor stock selection model through random forest to select stocks among the constituent stocks of the dominant style index, and buys the selected stocks according to the optimal portfolio weights determined by the principle of minimum variance. The empirical results show that the annualized rate of return of the strategy in the eight years from January 1, 2012 to April 1, 2020 is 3.6% higher than that of the single-round strategy, far exceeding the performance of the CSI 300 Index during the same period.


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    [1] H. Yuan, Overview of the application of data mining models in stock market prediction, Chin. Collect. Econ., 33 (2017), 66-67.
    [2] G. Quigley, R. Sinquefield, Performance of UK Equity Unit Trusts, J. Asset Manage., 1 (2000), 72-92. doi: 10.1057/palgrave.jam.2240006
    [3] K. Froot, M. Teo, Style investing and institutional investors, J. Financ. Quant. Anal., 43 (2008), 883-906. doi: 10.1017/S0022109000014381
    [4] J. Xiao, Y. X. Wang, W. Z. Chen, et al. Empirical Research on style level momentum strategy in the China stock market, Finance Econ., 03 (2006), 23-29.
    [5] Y. R. Guo, X. L. Wang, Rotational forecast of stock market over and under style based on BP neural network, Comput. Simulat., 36 (2019), 239-242.
    [6] P. S. Mohanram, Separating winners from losers among low Book-to-Market stocks using financial statement analysis, Rev. Account. Stud., 10 (2005), 133-170. doi: 10.1007/s11142-005-1526-4
    [7] F. Pan, Multi-factor Stock Selection Model Based on Effective Factors, Shenzhen: Anxin Securities Research Center, (2011), 1-21.
    [8] J. B. Guerard Jr., H. M. Markowitz, G. L. Xu, et al. Earnings forecasting in a global stock selection model and efficient portfolio construction and management, Int. J. Forecasting, 31 (2015), 550-560. doi: 10.1016/j.ijforecast.2014.10.003
    [9] V. DeMiguel, A. Martin-Utrera, F. J. Nogales, et al. A Transaction-Cost perspective on the multitude of firm characteristics, LBS Working Paper, 2017.
    [10] S. Kozak, S. Nagel, S. Santosh, et al. Shrinking the Cross- section, J. Financ. Econ., 135 (2020), 271-292. doi: 10.1016/j.jfineco.2019.06.008
    [11] L. Khaidem, S. Saha, S. R. Dey, et al. Predicting the direction of stock market prices using random forest, Appl. Math. Financ., (2016), 1-20.
    [12] J. B. Guerard, R. A. Gillam, H. Markowitz, et al. Data mining corrections testing in Chinese stocks, Interfaces, 48 (2018), 108-120. doi: 10.1287/inte.2017.0908
    [13] H. Phillip, Using autoregressive modelling and machine learning for stock market prediction and trading: ICICT 2018, Adv. Intel. Sys. Comput., 2 (2018), 767-774.
    [14] Z. F. Dai, H. Zhu, Stock return predictability from a mixed model perspective, J Pacific-Basin Financ. J., (2020), 101267.
    [15] Z. F. Dai, H. T. Zhou, Prediction of stock returns: Sum-of-the-Parts method and economic constraint method, Sustainability, 12 (2020), 541.
    [16] Z. H. Zhou, Machine Learning, Tsinghua University Press Bei Jing, 2016.
    [17] H. M. Markowitz, Portfolio selection, J. Financ., 7 (1952), 77-91.
    [18] H. M. Markowitz, Portfolio selection: Efficient diversification of investment, John Wiley and Sons, New York, 1959.
    [19] H. M. Markowitz, Mean-Variance analysis in portfolio choice and capital markets, Basil Blackwell, London, 1987.
    [20] J. B. Guerard, H. M. Markowitz, G. Xu, et al. Earnings forecasting in a global stock selection model and efficient portfolio construction and management, Int. J. Forecasting, 31 (2015), 550-560. doi: 10.1016/j.ijforecast.2014.10.003
    [21] H. M. Markowitz, Risk-Return analysis: The theory and practice of rational investing, McGrawHill, New York, 2013.
    [22] J. Z. Xu, Analysis of quantitative stock selection based on multi-factor model, Financ. Theor. Explorat., 3 (2017), 30-38.
    [23] H. Zhang, H. L. Shen, Y. C. Liu, et al. Research on Multi-factor Quantitative Stock Picking Problem Based on Self-Attention Neural Network. Mathematical Statistics and Management, 2020. Available from: https://doi.org/10.13860/j.cnki.sltj.20200403-001.
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