Predicting the Price of SP500 Index Based on Machine Learning Methods
- DOI
- 10.2991/978-94-6463-098-5_59How to use a DOI?
- Keywords
- Machine learning algorithm; Random Forest model; Support Vector Machine model
- Abstract
This paper mainly introduces the machine learning algorithm to predict the rise and fall of SP500 stock return prediction. Data of stock trading in the past 12 years (opening price, highest price, lowest price, and closing price) were adopted and preprocessed as sample data. Finally, nine technical parameters were adopted in Support Vector Machine and Random Forest models to predict the rise and fall of stocks. For parameters, it was divided into discrete variables, and continuous variables then are used. In the discrete variables and continuous variable part, the F1 score result of Support Vector Machine were 0.90 and 0.89, and the F1 score result of Random Forest were 0.91 and 0.96. Therefore, it can be concluded that the Random Forest model is better than the Support Vector Machine model.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Xing Wei PY - 2022 DA - 2022/12/27 TI - Predicting the Price of SP500 Index Based on Machine Learning Methods BT - Proceedings of the 2022 4th International Conference on Economic Management and Cultural Industry (ICEMCI 2022) PB - Atlantis Press SP - 527 EP - 534 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-098-5_59 DO - 10.2991/978-94-6463-098-5_59 ID - Wei2022 ER -