A hybrid approach to formulaic alpha discovery with large language model assistance
文献类型：期刊论文
| 作者 | Yu, Shuo1,3; Xue, Hong-Yan1,3; Ao, Xiang1,2,3; He, Qing1,3 |
| 刊名 | FRONTIERS OF COMPUTER SCIENCE
 |
| 出版日期 | 2026-02-01 |
| 卷号 | 20期号:2页码:14 |
| 关键词 | computational finance stock trend forecasting large language model |
| ISSN号 | 2095-2228 |
| DOI | 10.1007/s11704-025-41061-5 |
| 英文摘要 | In the domain of quantitative trading, the imperative is to translate historical financial data into predictive signals, commonly referred to as alpha factors, which serves to anticipate future market trends. Notably, formulaic alphas that are expressible via explicit mathematical formulas are highly sought after by certain investors for better interpretability. The evolving landscape of technology has witnessed the increasing deployment of large language models (LLMs) across various domains, which raises the question of whether LLMs can be effective in the context of formulaic alpha-mining tasks. This paper presents several paradigms aimed at integrating LLMs into the optimization loop of alpha mining, including scenarios where an LLM serves as the sole alpha generator, as well as instances where LLMs enhance existing frameworks. Empirical evaluations on real-world stock data demonstrate significant performance improvements, with our hybrid method achieving an average information coefficient (IC) of 0.0515, a 75% improvement over the baseline - a state-of-the-art reinforcement learning-based framework; backtesting further reveals a cumulative excess return more than double the baseline framework. These results underscore the potential of LLM-enhanced approaches in advancing formulaic alpha discovery and driving innovation in quantitative trading. |
| 资助项目 | National Key R&D Program of China[2022YFC3303302] ; National Natural Science Foundation of China[62476263] ; National Natural Science Foundation of China[U2436209] ; Project of Youth Innovation Promotion Association CAS, Beijing Nova Program[20230484430] ; Innovation Funding of ICT, CAS[E461060] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001596592800006 |
| 出版者 | HIGHER EDUCATION PRESS |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/41630]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Ao, Xiang; He, Qing |
| 作者单位 | 1.Chinese Acad Sci, Inst Comp Technol, Key Lab AI Safety, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 2.Inst Intelligent Comp Technol, Suzhou 215000, Peoples R China 3.Chinese Acad Sci, Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yu, Shuo,Xue, Hong-Yan,Ao, Xiang,et al. A hybrid approach to formulaic alpha discovery with large language model assistance[J]. FRONTIERS OF COMPUTER SCIENCE,2026,20(2):14. |
| APA | Yu, Shuo,Xue, Hong-Yan,Ao, Xiang,&He, Qing.(2026).A hybrid approach to formulaic alpha discovery with large language model assistance.FRONTIERS OF COMPUTER SCIENCE,20(2),14. |
| MLA | Yu, Shuo,et al."A hybrid approach to formulaic alpha discovery with large language model assistance".FRONTIERS OF COMPUTER SCIENCE 20.2(2026):14. |
入库方式： OAI收割
来源：计算技术研究所
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