# Leveraging LLMS for Top-Down Sector Allocation In Automated Trading

arXiv: https://arxiv.org/abs/2503.09647 · HTML: https://mlsanity.com/paper/2503.09647/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2025.

## Built on by (most cited first, 1 of 1)

- [Deriving Strategic Market Insights with Large Language Models: A Benchmark for Forward Counterfactual Generation](https://mlsanity.com/paper/2505.19430/) (2025)

## Abstract (arXiv)

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.