Zhiyuan Wei1, *, Yingxu Chen2, Meng Gao3, Yuancen Li4, Jianan Wan5, Yuqi Su6
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: firstname.lastname@example.org
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
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