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

Author

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

Abstract

The Federal Open Market Committee (FOMC) sets the federal funds rate, shaping monetary policy and the broader economy. We introduce \emph{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.

Suggested Citation

  • Yuhan Hou & Tianji Rao & Jeremy Tan & Adler Viton & Xiyue Zhang & David Ye & Abhishek Kodi & Sanjana Dulam & Aditya Paul & Yikai Feng, 2025. "FedSight AI: Multi-Agent System Architecture for Federal Funds Target Rate Prediction," Papers 2512.15728, arXiv.org.
    • Handle: RePEc:arx:papers:2512.15728
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    References listed on IDEAS

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    3. Sarno, Lucio & Thornton, Daniel L & Valente, Giorgio, 2005. "Federal Funds Rate Prediction," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 449-471, June.
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    5. Jungyeon Yoon & Juanjuan Fan, 2024. "Forecasting the direction of the Fed's monetary policy decisions using random forest," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2848-2859, November.
    6. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
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