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-sourced information synthesis and optimize decision-making outcomes through timely experience refinement remain unexplored. Here, we introduce the FinCon, an LLM-based multi-agent framework with CONceptual verbal reinforcement 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 single stock trading and portfolio management.

翻译：大型语言模型（LLM）在执行复杂任务方面已展现出显著潜力，并日益广泛应用于各类金融应用中。然而，高质量的序列化金融投资决策仍然具有挑战性。这些任务要求每次决策都与波动的环境进行多次交互，需要足够的智能以最大化收益并管理风险。尽管已有研究利用LLM开发出超越人类团队并取得可观投资回报的智能体系统，但通过及时的经验提炼来增强多源信息综合能力并优化决策结果的机会尚未得到充分探索。本文提出FinCon，一种基于LLM的多智能体框架，其通过针对多样化金融任务设计的CONceptual（概念性）语言强化机制实现性能提升。受现实世界中高效投资公司组织结构的启发，FinCon采用管理者-分析师沟通层级架构。该结构通过自然语言交互实现跨职能智能体为统一目标进行同步协作，并赋予每个智能体超越人类的记忆容量。此外，FinCon中的风险控制组件通过周期性启动自我批判机制来更新系统性投资信念，从而提升决策质量。这些概念化的信念可作为对未来智能体行为的语言强化，并可选择性地传播至需要知识更新的相应节点。该特性在显著提升性能的同时，减少了不必要的点对点通信成本。此外，FinCon在包括单股票交易与投资组合管理在内的多种金融任务中均展现出强大的泛化能力。