Paper Type
Complete
Paper Number
PACIS2025-1066
Description
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 FOMC meeting simulation framework MiniFed, which employs LLM agents to simulate real-world FOMC meeting members. 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. 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.
Recommended Citation
Seok, Sungil; Wen, Shuide; Yang, Qiyuan; Feng, Juan; and Yang, Wenming, "MiniFed: LLMs-based Agentic-Workflow for Simulating FOMC Meetings" (2025). PACIS 2025 Proceedings. 21.
https://aisel.aisnet.org/pacis2025/aiandml/aiandml/21
MiniFed: LLMs-based Agentic-Workflow for Simulating FOMC Meetings
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 FOMC meeting simulation framework MiniFed, which employs LLM agents to simulate real-world FOMC meeting members. 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. 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.
Comments
AI ML