FINCON: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Making

Large language models (LLMs) have shown potential in complex financial tasks, but sequential financial decision-making remains challenging due to the volatile environment and the need for intelligent risk management. While LLM-based agent systems have achieved impressive returns, optimizing multi-source information synthesis and decision-making through timely experience refinement is underexplored. We introduce FINCON, an LLM-based multi-agent framework with CONceptual verbal reinforcement for diverse FINancial tasks. Inspired by real-world investment firm structures, FINCON employs a manager-analyst hierarchy, enabling synchronized cross-functional agent collaboration towards unified goals via natural language interactions. Its dual-level risk-control component enhances decision-making by monitoring daily market risk and updating systematic investment beliefs through self-critique. These conceptualized beliefs provide verbal reinforcement for future decisions, selectively propagated to relevant agents, improving performance while reducing unnecessary peer-to-peer communication costs. FINCON generalizes well across tasks, including single stock trading and portfolio management.