The Application of Artificial Intelligence in A-Share Market Prediction: A Case Study of the CSI 300 Index

Main Article Content

Yao Tong
Institute of Business, Hunan University of Science and Technology, Xiangtan, China

DOI: https://doi.org/10.70267/aitia.2026441447

Keywords

SI 300 index, A-share market, artificial intelligence, stock prediction, sentiment analysis

Abstract

As a core component of the modern financial system, the stock market's price fluctuations not only reflect the business conditions of enterprises, the development trends of industries, and macroeconomic dynamics but also are closely related to investors' a sset allocation, market risk management, and national finance. With the development of the times, the application of artificial intelligence with strong data processing capabilities, nonlinear fitting capabilities, and autonomous learning capabilities in t he stock market can assist researchers in making more accurate predictions of stock price trends. This review summarizes and analyzes the application scenarios and technical paths of artificial intelligence in predicting the stock market. Through different models, hidden patterns are mined from massive data to provide more scientific decision-making bases for individual investors and institutions, reducing blind trading and irrational operations. At the same time, the combination of artificial intelligence and stock prediction promotes the upgrading of quantitative investment strategies and provides new technical methods for the industry.

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Published
Sep 13, 2026
Conference Proceedings Volume
Vol. 16 (2026): Proceedings of the 2026 International Conference on AI and Technological Innovation Applications (AITIA 2026)
Section
Articles
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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Tong, Yao. “The Application of Artificial Intelligence in A-Share Market Prediction: A Case Study of the CSI 300 Index”. Exploring Science Academic Conference Series, vol. 16, Sept. 2026, pp. 441-7, https://doi.org/10.70267/aitia.2026441447.