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.

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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  -