@inproceedings{kou-etal-2025-automate,
title = "Automate Strategy Finding with {LLM} in Quant Investment",
author = "Kou, Zhizhuo and
Yu, Holam and
Luo, Junyu and
Peng, Jingshu and
Li, Xujia and
Liu, Chengzhong and
Dai, Juntao and
Chen, Lei and
Han, Sirui and
Guo, Yike",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.1005/",
doi = "10.18653/v1/2025.findings-emnlp.1005",
pages = "18517--18533",
ISBN = "979-8-89176-335-7",
abstract = "We present a novel three-stage framework leveraging Large Language Models (LLMs) within a risk-aware multi-agent system for automate strategy finding in quantitative finance. Our approach addresses the brittleness of traditional deep learning models in financial applications by: employing prompt-engineered LLMs to generate executable alpha factor candidates across diverse financial data, implementing multimodal agent-based evaluation that filters factors based on market status, predictive quality while maintaining category balance, and deploying dynamic weight optimization that adapts to market conditions. Experimental results demonstrate the robust performance of the strategy in Chinese {\&} US market regimes compared to established benchmarks. Our work extends LLMs capabilities to quantitative trading, providing a scalable architecture for financial signal extraction and portfolio construction. The overall framework significantly outperforms all benchmarks with 53.17{\%} cumulative return on SSE50 (Jan 2023 to Jan 2024), demonstrating superior risk-adjusted performance and downside protection on the market."
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%0 Conference Proceedings
%T Automate Strategy Finding with LLM in Quant Investment
%A Kou, Zhizhuo
%A Yu, Holam
%A Luo, Junyu
%A Peng, Jingshu
%A Li, Xujia
%A Liu, Chengzhong
%A Dai, Juntao
%A Chen, Lei
%A Han, Sirui
%A Guo, Yike
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F kou-etal-2025-automate
%X We present a novel three-stage framework leveraging Large Language Models (LLMs) within a risk-aware multi-agent system for automate strategy finding in quantitative finance. Our approach addresses the brittleness of traditional deep learning models in financial applications by: employing prompt-engineered LLMs to generate executable alpha factor candidates across diverse financial data, implementing multimodal agent-based evaluation that filters factors based on market status, predictive quality while maintaining category balance, and deploying dynamic weight optimization that adapts to market conditions. Experimental results demonstrate the robust performance of the strategy in Chinese & US market regimes compared to established benchmarks. Our work extends LLMs capabilities to quantitative trading, providing a scalable architecture for financial signal extraction and portfolio construction. The overall framework significantly outperforms all benchmarks with 53.17% cumulative return on SSE50 (Jan 2023 to Jan 2024), demonstrating superior risk-adjusted performance and downside protection on the market.
%R 10.18653/v1/2025.findings-emnlp.1005
%U https://aclanthology.org/2025.findings-emnlp.1005/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.1005
%P 18517-18533
Markdown (Informal)
[Automate Strategy Finding with LLM in Quant Investment](https://aclanthology.org/2025.findings-emnlp.1005/) (Kou et al., Findings 2025)
ACL
- Zhizhuo Kou, Holam Yu, Junyu Luo, Jingshu Peng, Xujia Li, Chengzhong Liu, Juntao Dai, Lei Chen, Sirui Han, and Yike Guo. 2025. Automate Strategy Finding with LLM in Quant Investment. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 18517–18533, Suzhou, China. Association for Computational Linguistics.