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Predicting the Price of SP500 Index Based on Machine Learning Methods

In: Proceedings of the 2022 4th International Conference on Economic Management and Cultural Industry (ICEMCI 2022)

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  • Xing Wei

    (Northeastern University, Quantitative finance)

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.

Suggested Citation

  • Xing Wei, 2023. "Predicting the Price of SP500 Index Based on Machine Learning Methods," Advances in Economics, Business and Management Research, in: Hrushikesh Mallick & Gaikar Vilas B. & Ong Tze San (ed.), Proceedings of the 2022 4th International Conference on Economic Management and Cultural Industry (ICEMCI 2022), pages 527-534, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-098-5_59
    DOI: 10.2991/978-94-6463-098-5_59
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