@inproceedings{
yu2024fincon,
title={FinCon: A Synthesized {LLM} Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Making},
author={Yangyang Yu and Zhiyuan Yao and Haohang Li and Zhiyang Deng and Yuechen Jiang and Yupeng Cao and Zhi Chen and Jordan W. Suchow and Zhenyu Cui and Rong Liu and Zhaozhuo Xu and Denghui Zhang and Koduvayur Subbalakshmi and GUOJUN XIONG and Yueru He and Jimin Huang and Dong Li and Qianqian Xie},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dG1HwKMYbC}
}
Large language models (LLMs) have demonstrated notable potential in conducting complex tasks and are increasingly utilized in various financial applications. However, high-quality sequential financial investment decision-making remains challenging. These tasks require multiple interactions with a volatile environment for every decision, demanding sufficient intelligence to maximize returns and manage risks. Although LLMs have been used to develop agent systems that surpass human teams and yield impressive investment returns, opportunities to enhance multi-source information synthesis and optimize decision-making outcomes through timely experience refinement remain unexplored. Here, we introduce FinCon, an LLM-based multi-agent framework tailored for diverse financial tasks. Inspired by effective real-world investment firm organizational structures, FinCon utilizes a manager-analyst communication hierarchy. This structure allows for synchronized cross-functional agent collaboration towards unified goals through natural language interactions and equips each agent with greater memory capacity than humans. Additionally, a risk-control component in FinCon enhances decision quality by episodically initiating a self-critiquing mechanism to update systematic investment beliefs. The conceptualized beliefs serve as verbal reinforcement for the future agent’s behavior and can be selectively propagated to the appropriate node that requires knowledge updates. This feature significantly improves performance while reducing unnecessary peer-to-peer communication costs. Moreover, FinCon demonstrates strong generalization capabilities in various financial tasks, including stock trading and portfolio management.
FinCon: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Making
Yangyang Yu Zhiyuan Yao Haohang Li Zhiyang Deng Yuechen Jiang Yupeng Cao Zhi Chen Jordan W. Suchow Zhenyu Cui Rong Liu Zhaozhuo Xu Denghui Zhang Koduvayur Subbalakshmi GUOJUN XIONG Yueru He Jimin Huang Dong Li Qianqian Xie