Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 30, 24 January 2024
Open
Access | Article
Research on the selection of stock prediction models
* Author to whom correspondence should be addressed.
Abstract
Against the backdrop of increasing attention to the integration of machine learning and stock analysis, stock prediction models are a hot topic. The question this paper is studying in this study is which stock prediction model is more accurate in predicting stocks. The method of this study is based on the stock prices of new energy vehicle leader Tesla Motors in the past three years as a data set, using a random forest model and an SVR model to predict the stock prices over the next 10 days. Based on the parameter MSE values of the training models of two stock prediction models, compare their sizes to determine the accuracy and stability of the models. This study found that the stock prediction results of the SVR model are more accurate and stable than those of the random forest model. Therefore, it is believed that the stock prediction model using the SVR method will have more market value and occupy an important position in the integration of machine learning and stock trading analysis.
Keywords
Machine learning, stock forecast, SVR, random forest
References
1. Li, X. J., Xia, H. 2023, Research on Stock Price Regression Prediction Based on Machine Learning Algorithms. (Science and Technology Information, vol. 21), no. 14, pp. 227-231.
2. Li, F. M. 2023, Research on Stock Prediction Based on Machine Learning Fusion Model. (Lanzhou University).
3. Q, Q. M. Zhang, D. Wang, Y. Y. et al. 2022, The Application of Machine Learning in Stock Price Prediction. (China Market, vol. 21), pp. 7-10.
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5. Deng, J. L., Zhao, F. Q. Wang, X. X.. 2022, MTICA-AEO-SVR stock price prediction model. (Computer Engineering and Applications, vol. 58), no. 8, pp. 257-263
6. Cheng, H. 2023, Research on Stock Price Prediction Method Based on Deep Learning. (Shandong University of Business and Economics).
7. Li, Q. 2023, Research on Stock Price Prediction Based on RCI and GC-GAN. (Jiangxi University of Finance and Economics).
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 3rd International Conference on Computing Innovation and Applied Physics
- ISBN (Print)
- 978-1-83558-283-1
- ISBN (Online)
- 978-1-83558-284-8
- Published Date
- 24 January 2024
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/30/20241086
- Copyright
- 24 January 2024
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited