  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.
