Paper: arXiv 2503.09647

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.

Complexity vs Empirical Score

  • Math Complexity: 2.0/10
  • Empirical Rigor: 6.0/10 (capped from 6.5: no out-of-sample test; see penalty flags)
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper presents a methodology using existing LLM frameworks with a backtest showing specific performance metrics like Sharpe ratios, indicating strong empirical implementation, but the math is primarily descriptive of concepts like cross-sectional momentum and top-down investment without dense mathematical derivations.

Research Flowchart

  flowchart TD
  A["Research Goal: Top-Down Sector Allocation via LLMs"] --> B["Data Collection: Policy, Economic, & Sentiment Data"]
  B --> C["Methodology: LLM Processing of Macro Conditions"]
  C --> D["Computational Process: Sector-Level Allocation Model"]
  D --> E["Outcome: Sector Portfolio Construction"]
  E --> F["Key Findings: Sharpe 2.51 & Return 8.79%"]
  E --> G["Comparison: Outperforms Cross Momentum -0.61%"]

Cited References (41)

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  6. A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist — → 2402.18485
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and 33 more
  1. Can Large Language Models beat wall street? Evaluating GPT-4’s impact on financial decision-making with MarketSenseAI — → 2401.03737
  2. FinMem: A Performance-Enhanced LLM Trading Agent With Layered Memory and Character Design — → 2311.13743
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6 of 41 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.