Research on Stock and Futures Price Forecasting Based on Machine Learning
DOI:
https://doi.org/10.61173/n939gc07Keywords:
Futures Price Forecasting, Transformer, Prophet, XGBoostAbstract
Impact on their decision-making process. Applying machine learning techniques to futures price forecasting can assist investors in making more rational investment decisions. Based on this, this paper selects the trading data of the "CSI 300 Futures" from 9:31:00 on January 3, 2017, to 15:00:00 on December 31, 2021, as a sample, and uses the Transformer model, Prophet model, and XGBoost model to predict and analyze its stock price trends. The final evaluation results show that the XGBoost model performs best in terms of prediction accuracy, while the Transformer model shows greater potential.
References
[1] Taylor, S. J., & Letham, B. (2018) Forecasting at scale. The American Statistician, 72(1): 37–45.
[2] [2] Chen, T., & Guestrin, C. (2016) XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining: 785–794.
[3] [3] Vaswani, A., Shazeer, N., Parmar, N., et al. (2017) Attention is All You Need. Advances in Neural Information Processing Systems, 30: 5998–6008.
[4] [4] Zhang, Y., Qin, X., & Huang, T. (2019) A Hybrid Forecasting Framework for Financial Time Series. Expert Systems with Applications, 129: 132–142.
[5] Chen Haiqiang, Chen Liqiong, Li Yingxing, Luo Xiangfu (2025) Can high-frequency data improve stock price prediction? —— An empirical study based on functional data. In: China Financial Engineering Annual Conference. Beijing. Page number not provided.
[6] Hou Yanan (2025) Stock Price Prediction Based on Bi-LSTM Deep Learning. In: Global AI and Finance Summit, Shanghai. Page number not provided.
[7] Gumparthi, S., & Prasad, V. V. (2025). Using statistical and machine learning techniques for stock price prediction. Presented at the International Conference on Quantitative Trading, Singapore. No page number provided.
[8] WYL, S. (2025). Original stock prediction based on deep learning. In: Asian Fintech Innovation Forum, Hong Kong
Downloads
Published
Issue
Section
License
Copyright (c) 2025 by the authors.
This work is licensed under a Creative Commons Attribution 4.0 International License.
