Advances in Economics, Management and Political Sciences
- The Open Access Proceedings Series for Conferences
Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
Series Vol. 57 , 05 January 2024
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Access | Article
The Current Status and Future Prospects of Machine Learning in the Chinese Stock Market
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Abstract
This article explores the current status and prospects of machine learning techniques in the stock market of china. It begins by providing an overview of the relevant concepts of machine learning and analyzing its strengths and weaknesses in the stock market context. The article also introduces the current application of machine learning techniques in the gobal stock market. In the methodology section, several machine learning models that have performed well in the stock market are listed. The article then summarizes and analyzes the specific applications of machine learning and its algorithms in the stock market of China, highlighting the advantages and limitations due to the unique characteristics of the market.Finally, the article concludes by summarizing the aforementioned content and providing an outlook on the future development direction of machine learning technology in the stock market of chinese. While the performance of machine learning technology in the stock market has its pros and cons, it is undeniable that it holds an important position in the future development of the financial market, especially within the wave of innovation and progress in the financial industry.
Keywords
machine learning, China stock markets, financial technology
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
- ISBN (Print)
- 978-1-83558-205-3
- ISBN (Online)
- 978-1-83558-206-0
- Published Date
- 05 January 2024
- Series
- Advances in Economics, Management and Political Sciences
- ISSN (Print)
- 2754-1169
- ISSN (Online)
- 2754-1177
- DOI
- 10.54254/2754-1169/57/20230633
- Copyright
- 05 January 2024
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited