Generative AI-enhanced Sector-based Investment Portfolio Construction

Battle Count: 124

Highly relevant to quantitative trading as it directly addresses stock selection, portfolio construction, and risk-adjusted performance evaluation. The hybrid LLM + mean-variance optimization framework is directly applicable to systematic portfolio management. The sector-level analysis and regime-dependent performance findings are practically useful for quantitative strategists. However, the paper focuses on portfolio construction rather than high-frequency trading or execution.

Moderate complexity. The core methodology involves API calls to LLMs with structured prompts, followed by standard mean-variance optimization (well-established in quantitative finance). The multi-model, multi-sector framework requires significant computational orchestration (12 models × 11 sectors × multiple runs). Data validation and sector universe reconstruction add complexity. However, individual components (LLM prompting, Markowitz optimization) are well-understood.

The paper provides detailed prompt structures, methodology descriptions, and data sources (Yahoo Finance, S&P Global methodology). However, LLM outputs are inherently stochastic, and the exact API parameters, temperature settings, and full prompt templates are not entirely specified. The sector universe reconstruction process is described but relies on multiple public sources. No code repository is mentioned.