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展现出显著潜力，并日益广泛应用于各类金融场景。然而，高质量的序列化金融投资决策仍具挑战性。此类任务需在波动环境中为每个决策进行多次交互，要求足够的智能水平以实现收益最大化与风险管控。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，但通过实时经验精炼来增强多源信息整合与优化决策结果的可能性尚未得到充分探索。本文提出FinCon，一个基于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架构启发，FinCon采用管理者-分析师沟通层级结构。该架构通过自然语言交互实现跨职能智能体为统一目标同步协作，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。这些概念化信念将作为未来智能体行为的语言强化信号，并可选择性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，有效降低了不必要的点对点通信成本。实验表明，FinCon在个股交易与投资组合管理等多元金融任务中均展现出强大的泛化能力。