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使用 LLM 智能体模拟 FOMC 会议

文章 arXiv papers · 作者: Sungil Seok et al.
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总结

MiniFed 是一个使用大型语言模型智能体模拟联邦公开市场委员会会议的框架。它旨在呈现会议成员及委员会的决策过程，将关注点从仅研究联邦基金利率变化的影响，转向模拟利率决策如何形成。该框架采用五阶段工作流，并尝试优化模拟委员会的结构。

论文报告的实验显示，该框架能够高精度预测联邦基金利率，其智能体行为也与现实中的对应对象相符。说明没有提供数据集、评估指标、预测期限或对比基准的细节，因此无法在此评估这些结果的可靠性和普遍性。该研究涉及政策决策模拟和利率预测；它没有提出直接的交易策略，也没有证明能够获得市场收益。

核心观点

  • MiniFed 使用 LLM 智能体对 FOMC 会议的成员和流程建模。
  • 其五阶段工作流旨在模拟委员会审议过程并优化委员会结构。
  • 该框架既用于联邦基金利率预测，也用于重建会议决策过程。
  • 报告的实验声称预测准确且行为与现实相符，但现有说明未提供评估细节。
  • 本文聚焦政策模拟，而非特定的市场交易策略。

标签

机器学习固定收益美国市场统计学
全文
# MiniFed : Integrating LLM-based Agentic-Workflow for Simulating FOMC Meeting


# MiniFed : Integrating LLM-based Agentic-Workflow for Simulating FOMC Meeting









The Federal Funds rate in the United States plays a significant role in both domestic and international financial markets. However, research has predominantly focused on the effects of adjustments to the Federal Funds rate rather than on the decision-making process itself. Recent advancements in large language models(LLMs) offer a potential method for reconstructing the original FOMC meetings, which are responsible for setting the Federal Funds rate. In this paper, we propose a five-stage FOMC meeting simulation framework, MiniFed, which employs LLM agents to simulate real-world FOMC meeting members and optimize the FOMC structure. This framework effectively revitalizes the FOMC meeting process and facilitates projections of the Federal Funds rate. Experimental results demonstrate that our proposed MiniFed framework achieves both high accuracy in Federal Funds rate projections and behavioral alignment with the agents' real-world counterparts. Given that few studies have focused on employing LLM agents to simulate large-scale real-world conferences, our work can serve as a benchmark for future developments.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。