A Review of Large Language Models for Stock Price Forecasting from a Hedge-Fund Perspective
A comprehensive review examines how large language models are being applied to stock price forecasting in quantitative finance, with particular emphasis on practical challenges often overlooked in academic literature. The analysis, framed from a hedge-fund perspective, addresses critical implementation issues including sentiment analysis fragility, data leakage risks, and market friction constraints that affect real-world trading performance.