A Review of Large Language Models for Stock Price Forecasting from a Hedge-Fund Perspective
It provides a practical guide for hedge fund managers and researchers on integrating LLMs into real-world trading pipelines, but is a review without novel empirical results.
This review surveys LLM applications for stock price forecasting from a hedge-fund perspective, highlighting practical pitfalls like data leakage and illiquidity premia that are often overlooked in academic literature.
Large language models (LLMs) are increasingly deployed in quantitative finance for stock price forecasting. This review synthesizes recent applications of LLMs in this domain, including extracting sentiment from financial news and social media, analyzing financial reports and earnings-call transcripts, tokenizing or symbolizing stock price series, and constructing multi-agent trading systems. Particular attention is paid to practical pitfalls that are often understated in the literature, such as fragility in sentiment analysis, dataset and horizon design, performance evaluation metrics, data leakage, illiquidity premia, and limits of stock price predictability. Organized from a hedge-fund perspective, the review is intended to guide both academic researchers and hedge fund managers in integrating LLMs into real-world trading pipelines and in stress-testing their robustness under realistic market frictions.