A stock selection algorithm with hybrid support vector regression and differential evolutionary

Author:

Wang Ruizhe1ORCID

Affiliation:

1. School of Finance, Central University of Finance and Economics, China

Publisher

ACM

Reference26 articles.

1. Guo, Y., Han, S., Shen, C., Li, Y., Yin, X., & Bai, Y.: An Adaptive SVR for High-Frequency Stock Price Forecasting. 6, pp. 11397-11404, IEEE Access, 2018.

2. Das, S. P., & Padhy, S.: Unsupervised extreme learning machine and support vector regression hybrid model for predicting energy commodity futures index. 9, pp. 333-346, Memetic Computing, 2017.

3. Alberdi, R., & Khandelwal, K.: Comparison of robustness of metaheuristic algorithms for steel frame optimization. 102, pp. 40-60, Engineering Structures, 2015.

4. Absalom, E. E., Shukla, A. K., Nath, R., Akinyelu, A. A., Agushaka, J., Chiroma, H., ... & Muhuri, P. K.: Metaheuristics: a comprehensive overview and classification along with bibliometric analysis. 54, pp. 4237-4316, Artificial Intelligence Review, 2021.

5. Alsarhan, A., Alauthman, M., Alshdaifat, E., Al-Ghuwairi, A., & Al-Dubai, A.: Machine Learning-driven optimization for SVM-based intrusion detection system in vehicular ad hoc networks. 14, pp. 6113-6122, Journal of Ambient Intelligence and Humanized Computing, 2021.

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