从对冲基金视角看大模型如何预测股价及潜在陷阱
A Review of Large Language Models for Stock Price Forecasting from a Hedge-Fund Perspective
- 结合金融新闻、财报和交易数据，用大模型提取市场情绪与模式
- 指出情感分析脆弱性、数据泄露等实际应用中的关键风险
- 适合对冲基金从业者和量化研究者参考真实市场限制
大语言模型（LLMs）在量化金融中日益用于股票价格预测。本文综述了近期在该领域的应用，包括从财经新闻和社交媒体中提取情绪、分析财务报告与财报电话会议记录、将股价序列分词或符号化，以及构建多智能体交易系统。特别关注文献中常被低估的实际挑战，如情感分析的脆弱性、数据集与预测时长设计、性能评估指标、数据泄露、流动性溢价，以及股价可预测性的局限。本文从对冲基金视角出发，旨在指导学术研究者与对冲基金经理将大模型整合进真实交易流程，并在现实市场摩擦下测试其鲁棒性。
原文摘要 · Abstract (English)
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.
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