A Review of Large Language Models for Stock Price Forecasting from a Hedge-Fund Perspective

Battle Count: 77

Highly relevant to quantitative trading. The paper is explicitly written from a hedge-fund perspective, addressing practical deployment concerns that academic papers often overlook. It covers the full pipeline from data ingestion (sentiment, financial reports, price series) through model design (tokenization, multi-agent systems) to evaluation (risk-adjusted metrics, illiquidity, data leakage). The practical guidelines for integrating LLMs into production-grade trading pipelines are directly actionable for quantitative trading teams.

As a review paper, it does not describe a single implementation. However, the systems it reviews (multi-agent LLM trading frameworks, tokenization pipelines, RAG-augmented agents, regime-switching models) are highly complex to implement in production. The paper emphasizes that real-world deployment requires handling data leakage, illiquidity, regime changes, and robust evaluation—each adding significant engineering complexity. Frameworks like LangChain, AutoGen, and CrewAI are mentioned as tools to reduce some complexity.

As a review/survey paper, it does not propose a specific reproducible pipeline. It references numerous studies with varying levels of reproducibility. No code or specific experimental setup is provided by the authors themselves. The paper discusses reproducibility issues in the literature (data leakage, dataset design, evaluation metrics) but does not offer a reproducible implementation.