• ISSN: 2301-3567 (Print), 2972-3981 (Online)
    • Abbreviated Title: J. Econ. Bus. Manag.
    • Frequency: Quarterly
    • DOI: 10.18178/JOEBM
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HOME > Archive > 2026 > Volume 14 Number 3 (2026) >
JOEBM 2026 Vol.14(3): 184-186
DOI: 10.18178/joebm.2026.14.3.934

A Review of Technical Methods of Machine Learning for Stock Prediction in A-share Market

Kaiyuan Xiang
Kaiyuan Xiang
School of Economics, Guangdong University of Technology, Guangdong, China
Email: 13827223839@163.com

Manuscript received April 29, 2026; accepted May 20, 2026; published September 2, 2026.

Abstract—This paper systematically sorts out the evolution and application of machine learning technical methods for A-share stock prediction, analyzing algorithm architecture, model principles, and market adaptability. The study finds that the application of machine learning in the A-share market has gone through three development stages: from traditional statistical methods to deep learning, and then to the era of large models, forming a complete technical system. Especially for the A-share market’s unique institutions, substantial targeted technological innovations have emerged. This paper analyzes recent research, reveals technological breakthroughs and application value, and prospects future trends. The research shows that machine learning has significantly improved the prediction accuracy of the A-share market, among which the out-of-sample R² of deep learning models can reach 7.27%, while that of traditional linear models is only 3.46%. At the same time, this paper also points out the technical limitations, providing an important reference for relevant research.

Keywords—machine learning, A-share market, stock prediction, deep learning

Cite: Kaiyuan Xiang, "A Review of Technical Methods of Machine Learning for Stock Prediction in A-share Market," Journal of Economics, Business and Management, vol. 14, no. 3, pp. 184-186, 2026.

Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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