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.
@article{arxiv.2503.09647,
title = {Leveraging LLMS for Top-Down Sector Allocation In Automated Trading},
author = {Ryan Quek Wei Heng and Edoardo Vittori and Keane Ong and Rui Mao and Erik Cambria and Gianmarco Mengaldo},
journal= {arXiv preprint arXiv:2503.09647},
year = {2025}
}