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

TL;DR

This review explores how large language models are used in quantitative finance for stock prediction, highlighting practical challenges and guiding hedge fund applications.

Contribution

It synthesizes recent LLM applications in finance, emphasizing real-world issues and providing guidance for hedge fund integration and robustness testing.

Findings

LLMs extract sentiment from financial news and social media.

Analysis of financial reports and earnings calls using LLMs.

Discussion of practical pitfalls like data leakage and market frictions.

Abstract

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.