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.

翻译：本文提出一种利用大型语言模型（LLMs）通过系统分析宏观经济状况与市场情绪进行行业层面投资组合配置的方法。我们的框架通过同时处理政策文件、经济指标和情绪模式等多重数据流，强调自上而下的行业配置策略。实证结果表明，相较于传统横截面动量策略，本方法实现了更优的风险调整收益：夏普比率达到2.51，投资组合收益率为8.79%，而传统策略对应指标分别为-0.61和-1.39%。这些结果表明，基于LLM的系统化宏观分析为增强行业层面的自动化投资组合配置决策提供了可行路径。