Research on the Application of Artificial Intelligence in Quantitative Investment: Implementation Scenarios, Practical Challenges, and Future Trends
The rapid advancement of artificial intelligence (AI) technology has brought revolutionary changes to the field of quantitative investment. This study systematically examines the application scenarios, practical challenges, and future trends of AI in quantitative investment. First, the paper reviews...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | SHS Web of Conferences |
| Online Access: | https://www.shs-conferences.org/articles/shsconf/pdf/2025/09/shsconf_icdde2025_02022.pdf |
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| _version_ | 1839639388942761984 |
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| author | Chen Boshan |
| author_facet | Chen Boshan |
| author_sort | Chen Boshan |
| collection | DOAJ |
| description | The rapid advancement of artificial intelligence (AI) technology has brought revolutionary changes to the field of quantitative investment. This study systematically examines the application scenarios, practical challenges, and future trends of AI in quantitative investment. First, the paper reviews the evolutionary trajectory of AI technologies, spanning from early expert systems to contemporary deep reinforcement learning, while analyzing breakthrough developments in specialized financial AI tools and core technical capabilities. Second, the research focuses on key AI applications in quantitative investment, including multi-factor model optimization, high-frequency market risk management, multimodal data integration, and algorithmic trading enhancement. Empirical evidence demonstrates that AI technologies can significantly improve strategy performance and expand the boundaries of traditional methodologies. However, AI applications still face challenges such as model overfitting, interpretability limitations, regulatory lag, and computational costs. Finally, the paper outlines future trends in technological convergence and application scenario development. This research provides a systematic framework for understanding the paradigm shift in quantitative investment driven by AI, while offering practical references for institutional investors, individual users, and regulatory bodies. |
| format | Article |
| id | doaj-art-dfee48bab2c842ea9a752ec87f5ee2b0 |
| institution | Matheson Library |
| issn | 2261-2424 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | SHS Web of Conferences |
| spelling | doaj-art-dfee48bab2c842ea9a752ec87f5ee2b02025-07-04T09:35:48ZengEDP SciencesSHS Web of Conferences2261-24242025-01-012180202210.1051/shsconf/202521802022shsconf_icdde2025_02022Research on the Application of Artificial Intelligence in Quantitative Investment: Implementation Scenarios, Practical Challenges, and Future TrendsChen Boshan0School of Finance, Tianjin University of Finance and EconomicsThe rapid advancement of artificial intelligence (AI) technology has brought revolutionary changes to the field of quantitative investment. This study systematically examines the application scenarios, practical challenges, and future trends of AI in quantitative investment. First, the paper reviews the evolutionary trajectory of AI technologies, spanning from early expert systems to contemporary deep reinforcement learning, while analyzing breakthrough developments in specialized financial AI tools and core technical capabilities. Second, the research focuses on key AI applications in quantitative investment, including multi-factor model optimization, high-frequency market risk management, multimodal data integration, and algorithmic trading enhancement. Empirical evidence demonstrates that AI technologies can significantly improve strategy performance and expand the boundaries of traditional methodologies. However, AI applications still face challenges such as model overfitting, interpretability limitations, regulatory lag, and computational costs. Finally, the paper outlines future trends in technological convergence and application scenario development. This research provides a systematic framework for understanding the paradigm shift in quantitative investment driven by AI, while offering practical references for institutional investors, individual users, and regulatory bodies.https://www.shs-conferences.org/articles/shsconf/pdf/2025/09/shsconf_icdde2025_02022.pdf |
| spellingShingle | Chen Boshan Research on the Application of Artificial Intelligence in Quantitative Investment: Implementation Scenarios, Practical Challenges, and Future Trends SHS Web of Conferences |
| title | Research on the Application of Artificial Intelligence in Quantitative Investment: Implementation Scenarios, Practical Challenges, and Future Trends |
| title_full | Research on the Application of Artificial Intelligence in Quantitative Investment: Implementation Scenarios, Practical Challenges, and Future Trends |
| title_fullStr | Research on the Application of Artificial Intelligence in Quantitative Investment: Implementation Scenarios, Practical Challenges, and Future Trends |
| title_full_unstemmed | Research on the Application of Artificial Intelligence in Quantitative Investment: Implementation Scenarios, Practical Challenges, and Future Trends |
| title_short | Research on the Application of Artificial Intelligence in Quantitative Investment: Implementation Scenarios, Practical Challenges, and Future Trends |
| title_sort | research on the application of artificial intelligence in quantitative investment implementation scenarios practical challenges and future trends |
| url | https://www.shs-conferences.org/articles/shsconf/pdf/2025/09/shsconf_icdde2025_02022.pdf |
| work_keys_str_mv | AT chenboshan researchontheapplicationofartificialintelligenceinquantitativeinvestmentimplementationscenariospracticalchallengesandfuturetrends |