Analysis of Artificial Intelligence-Driven Intelligent Investment Strategies and Their Market Effects
- DOI
- 10.2991/978-94-6463-734-2_8How to use a DOI?
- Keywords
- artificial intelligence; intelligent investment strategy; market effect; machine learning; quantitative investment
- Abstract
With the rapid development of technology, the application of artificial intelligence in the field of finance is becoming increasingly widespread. This paper deeply explores artificial intelligence-driven intelligent investment strategies, including the application of machine learning algorithms in investment decisions, the combination of quantitative investment and artificial intelligence, and the analysis of financial market information by natural language processing technology. At the same time, it analyzes in detail the market effects of intelligent investment strategies, such as improving investment efficiency, reducing risks, and improving market liquidity. Through the study of practical cases and theoretical analysis, it provides valuable references for investors and financial institutions and looks forward to the future development trend of artificial intelligence in the investment field.
- Copyright
- © 2025 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Mingze Qin PY - 2025 DA - 2025/05/27 TI - Analysis of Artificial Intelligence-Driven Intelligent Investment Strategies and Their Market Effects BT - Proceedings of the 2025 10th International Conference on Social Sciences and Economic Development (ICSSED 2025) PB - Atlantis Press SP - 67 EP - 77 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-734-2_8 DO - 10.2991/978-94-6463-734-2_8 ID - Qin2025 ER -