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Leveraging LLMS for Top-Down Sector Allocation In Automated Trading

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  • Ryan Quek Wei Heng
  • Edoardo Vittori
  • Keane Ong
  • Rui Mao
  • Erik Cambria
  • Gianmarco Mengaldo

Abstract

This paper introduces a methodology leveraging Large Language Models (LLMs) for sector-level portfolio allocation through systematic analysis of macroeconomic conditions and market sentiment. Our framework emphasizes top-down sector allocation by processing multiple data streams simultaneously, including policy documents, economic indicators, and sentiment patterns. Empirical results demonstrate superior risk-adjusted returns compared to traditional cross momentum strategies, achieving a Sharpe ratio of 2.51 and portfolio return of 8.79% versus -0.61 and -1.39% respectively. These results suggest that LLM-based systematic macro analysis presents a viable approach for enhancing automated portfolio allocation decisions at the sector level.

Suggested Citation

  • Ryan Quek Wei Heng & Edoardo Vittori & Keane Ong & Rui Mao & Erik Cambria & Gianmarco Mengaldo, 2025. "Leveraging LLMS for Top-Down Sector Allocation In Automated Trading," Papers 2503.09647, arXiv.org, revised Mar 2025.
    • Handle: RePEc:arx:papers:2503.09647
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