Comparative Study of Machine Learning Techniques for Stock Market Price and Optimizing Its Cumulative Strategy Returns
Author:
Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-34644-6_4
Reference17 articles.
1. Bazrkar, M. J., & Hosseini, S. (2022). Predict stock prices using supervised learning algorithms and particle swarm optimization algorithm. Computational Economics, 1–22.
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3. Dash, R. K., Nguyen, T. N., Cengiz, K., & Sharma, A. (2021). Fine-tuned support vector regression model for stock predictions. Neural Computing and Applications, 1–15.
4. Gong, X. L., Liu, X. H., Xiong, X., & Zhuang, X. T. (2019). Forecasting stock volatility process using improved least square support vector machine approach. Soft Computing, 23(22), 11867–11881.
5. Gowthul Alam, M. M., & Baulkani, S. (2019). Local and global characteristics-based kernel hybridization to increase optimal support vector machine performance for stock market prediction. Knowledge and Information Systems, 60(2), 971–1000 (2019)
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