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Research on Quantitative Stock Selection Method Based on Random Forest

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LISS 2020

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Abstract

Stock investment has always been a hot issue in the financial and investment fields. The research of stock quantitative investment method based on modern data mining method and machine learning technology is a brand new field. Random forest is a prediction method integrating multiple decision trees. This paper studies the application of random forest in the quantitative stock selection of stocks, selects the annual report data of China and Shenzhen 300 constituent stocks from 2014 to 2018, and compares the prediction of stock investment returns by using decision tree and random forest method respectively. The applicability and prediction performance of the random forest method are verified, and the importance and interrelationship of annual report indicators are preliminary explored.

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References

  1. H.L. Xie, D. Hu, The application of multi-factor quantitative model in investment portfolio—comparative study based on LASSO and elastic net. Stat. Inf. Forum 32(10), 36–42 (2017)

    Google Scholar 

  2. B. Li, Y. Lin, ML-TEA: a set of quantitative investment algorithms based on machine learning and technical analysis. Syst. Eng. Theory Pract. 37(5), 1089–1100 (2017)

    Google Scholar 

  3. R.D. Chen, H.H. Yu, Stock selection model of support vector machine based on heuristic algorithm. Syst. Eng. 32(2) (2014)

    Google Scholar 

  4. Z. Su, X.Y. Fu, Kernel principal component genetic algorithm and improvement of SVR stock selection model. Stat. Res. 30(5), 54–62 (2013)

    Google Scholar 

  5. Z.F. Cao, H. Ji, B.C. Xie, Using random forest algorithm to realize selection of good stocks. J. Capital Univ. Econ. Bus. 16(2), 21–27 (2014)

    Google Scholar 

  6. F.H. Chen, An alternative model for the analysis of detecting electronic industries earnings management using stepwise regression, random forest, and decision tree. Soft. Comput. 20(5), 1945–1960 (2016)

    Article  Google Scholar 

  7. J. Sun, J. Lang, H. Fujita, H. Li, Imbalanced enterprise credit evaluation with DTE-SBD: decision tree ensemble based on SMOTE and bagging with differentiated sampling rates. Inf. Sci. 425, 76–91 (2018)

    Article  Google Scholar 

  8. A.G. Malliaris, M. Malliaris, What drives gold returns? A decision tree analysis. Financ. Res. Lett. 13, 45–53 (2015)

    Article  Google Scholar 

  9. Q. Caoa, K.B. Leggioa, M.J. Schniederjans, A comparison between Fama and French’s model and artificial neural networks in predicting the Chinese stock market. Comput. Oper. Res. 32, 2499–2512 (2015)

    Article  Google Scholar 

  10. R.A. Kamble, Short and long term stock trend prediction using decision tree, in International Conference on Intelligent Computing and Control Systems (ICICCS) (2017)

    Google Scholar 

  11. A. Al-Nasseria, A. Tuckerb, S. Cesarea, Quantifying StockTwits semantic terms’ trading behavior in financial markets: an effective application of decision tree algorithms. Expert Syst. Appl. 42(23), 9192–9210 (2015)

    Article  Google Scholar 

  12. B. Leo, Statistical modeling: the two cultures. Stat. Sci. 16(3), 199–231 (2001)

    Article  Google Scholar 

  13. M. Kumar, M. Thenmozhi, Forecasting stock index movement: a comparison of support vector machines and random forest, in Indian Institute of Capital Markets 9th Capital Markets Conference Paper (2006)

    Google Scholar 

  14. A. Montillo, H. Ling, Age regression from faces using random forest, in 16th IEEE International Conference (2009), pp. 2465–2468

    Google Scholar 

  15. V.F. Rodriguez-Galiano, B. Ghimire, J. Rogan, M. Chica-Olmo, J.P. Rigol-Sanchez, An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS J. Photogrammetry Remote Sens. 67, 93–104 (2012)

    Article  Google Scholar 

  16. K. Theofilatos, S. Likothanassis, A. Karathanasopoulos, Modeling and trading the EUR/USD exchange rate using machine learning techniques. ETASR Eng. Technol. Appl. Sci. Res. 2(5), 269–272 (2012)

    Article  Google Scholar 

  17. Q. Qin, Q.G. Wang, J. Li, S.S. Ge, Linear and nonlinear trading models with gradient boosted random forest and application to Singapore stock market. J. Intell. Learn. Syst. Appl. 5, 1–10 (2013)

    Google Scholar 

  18. Z.H. Zhou, Machine Learning (Tsinghua University Press, Beijing, 2016), pp. 178–180

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Economics and Management, University of Science and Technology Beijing, Beijing, China

    Haining Yang & Xuedong Gao

Authors
  1. Haining Yang
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  2. Xuedong Gao
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Corresponding author

Correspondence to Haining Yang .

Editor information

Editors and Affiliations

  1. Beijing Jiaotong University, Beijing, China

    Shifeng Liu

  2. Budapest University of Technology and Economics, Budapest, Hungary

    Gábor Bohács

  3. Beijing Jiaotong University, Beijing, China

    Xianliang Shi

  4. International Center for Informatics Research, Beijing Jiaotong University, Beijing, China

    Xiaopu Shang

  5. Beijing Logistics Informatics Research Base, Beijing Jiaotong University, Beijing, China

    Anqiang Huang

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Yang, H., Gao, X. (2021). Research on Quantitative Stock Selection Method Based on Random Forest. In: Liu, S., Bohács, G., Shi, X., Shang, X., Huang, A. (eds) LISS 2020. Springer, Singapore. https://doi.org/10.1007/978-981-33-4359-7_36

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