A hybrid approach to formulaic alpha discovery with large language model assistance

doi:10.1007/s11704-025-41061-5

	A hybrid approach to formulaic alpha discovery with large language model assistance
	Yu, Shuo 1,2; Xue, Hong-Yan 1,2; Ao, Xiang 1,2,3; He, Qing 1,2
	2026-02-01
发表期刊	FRONTIERS OF COMPUTER SCIENCE
ISSN	2095-2228
卷号	20 期号:2 页码:14
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.
关键词	computational finance stock trend forecasting large language model
DOI	10.1007/s11704-025-41061-5
收录类别	SCI
语种	英语
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
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods
WOS记录号	WOS:001596592800006
出版者	HIGHER EDUCATION PRESS
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/41631
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.Chinese Acad Sci, Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Inst Intelligent Comp Technol, Suzhou 215000, 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.