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

Stock Prediction Methods based on Ensemble Learning

Author(s)

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

Corresponding Author:
Zhiyuan Wei
Affiliation(s)

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: andywei1003@gmail.com

These authors contributed equally to this work

Abstract

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.

Keywords

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.

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