Welcome to Francis Academic Press

Academic Journal of Business & Management, 2021, 3(6); doi: 10.25236/AJBM.2021.030619.

Stock Prediction Methods based on Ensemble Learning


Zhiyuan Wei1, *, Yingxu Chen2, Meng Gao3, Yuancen Li4, Jianan Wan5, Yuqi Su6

Corresponding Author:
Zhiyuan Wei

1University of Texas at Austin, Austin, TX, United States

2Henan Experimental High School, Zhengzhou, Henan, China

3Shandong Normal University, Jinan, Shandong, China

4Hefei No.1 High School, Hefei, Anhui, China

5Zhejiang International Studies University, Hangzhou, Zhejiang, China

6Xi'an University of Technology, Xi'an, Shaanxi, China

*Corresponding author: [email protected]

These authors contributed equally to this work


With the rapid development of stock market, there have been large interests in stock prediction. The decision making based on rational and logical analysis as well as forecast often has a very positive supporting effect, reducing investment risk while enhancing the profits. The development of technology has led to a variety of mature machine learning models for predicting the stock market such as the support vector machine (SVM) model and support vector regression (SVR) model, which will be introduced later in the paper. In this paper, it focuses on the improvement of the existing machine learning models by comparing the deviation and coefficient of curves of different stocks. The experiment indicates that the ensemble models provide more effective and more accurate stock prediction compared with only using the SVR model.


stock prediction, SVR, machine learning models, ensemble learning

Cite This Paper

Zhiyuan Wei, Yingxu Chen, Meng Gao, Yuancen Li, Jianan Wan, Yuqi Su. Stock Prediction Methods based on Ensemble Learning. Academic Journal of Business & Management (2021) Vol. 3, Issue 6: 113-118. https://doi.org/10.25236/AJBM.2021.030619.


[1] Huai Jiang, 2009. Research on the Development of China's Stock Market. International Economic Cooperation, 2009(07).

[2] Teng Wang, 2013. Analysis of the Current Situation of China's Stock Market. Chinese and Foreign Entrepreneurs, 2013(18).

[3] Zhenquan Zhao, et al, 2001. Appropriate Regulation Size of Chinese Securities Market. China 2001: Economic Situation Analysis and Forecast.

[4] Haoran Xu, et al, 2020. Analysis on Application of Machine Learning in Stock Forecasting. Computer Engineering and Applications. 56(12): 19-24.  

[5] Vapnik & Vladimir, 2013. The Nature of Statistical Learning Theory. Springer Science & Business Media.

[6] Wangshu Peng, 2013. Comparison of Stock Index Prediction Models Based on BP Neural Network and Support Vector Machine. 2013 Financial Markets, 1007-9041-2013(01)-0071-02.

[7] Wenjuan Mai, et al, 2018. Stock Price Prediction Based on ARIMA-SVM Model. 2018 International Conference on Big Data and Artificial Intelligence.

[8] Wei Huang, et al, 2005. Forecasting Stock Market Movement Direction with Support Vector Machine. Computers & Operations Research 32.10 (2005): 2513-2522.

[9] Shijun Zhang, 2014. Stock Price Prediction Base on Network Public Opinion an Support Vector Machine. Nanjing University of Information Science & Technology.

[10] Trafalis, et al, 2002. Benders Decomposition Technique for Support Vector Regression. Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No. 02CH37290). Vol. 3. IEEE.

[11] Shanqing Yang, 2018. Stock Price Prediction Based on Support Vector Regression and Differential Evolution. Nanchang University.

[12] Ersu Biao, 2007. Research on the Stock Price Prediction. Tianjin University.