Leveraging LLMS for Top-Down Sector Allocation In Automated Trading

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

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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.

Date: 2025-03, Revised 2025-03
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