FedSight AI: Multi-Agent System for Federal Funds Target Rate Prediction

Adler Viton, Tianji Rao, Yuhan Hou, Jeremy Matthew Tan, Xiyue Zhang, David Ye, Abhishek Kodi, Sanjana Dulam, Aditya Paul, Yikai Feng

Keywords: Federal Fund Rates, Artificial Intelligence, Multi-agent System, Chain-of-Draft

TL;DR: FedSight AI is a multi-agent system that simulates FOMC deliberations using LLMs to integrate structured economic data and unstructured narratives, achieving accurate and interpretable forecasts of federal funds rate decisions.

Abstract: The Federal Open Market Committee (FOMC) sets the federal funds rate, shaping monetary policy and the broader economy. We introduce FedSight AI, a multi-agent framework that uses large language models (LLMs) to simulate FOMC deliberations and predict policy outcomes. Member agents analyze structured indicators and unstructured inputs such as the Beige Book, debate options, and vote, replicating committee reasoning. A Chain-of-Draft (CoD) extension further improves efficiency and accuracy by enforcing concise multistage reasoning. Evaluated at 2023-2024 meetings, FedSight CoD achieved accuracy of 93.75\% and stability of 93.33\%, outperforming baselines including MiniFed and Ordinal Random Forest (RF), while offering transparent reasoning aligned with real FOMC communications.

Submission Number: 104