Natural Language Processing for Asset Managers: Turning Text into Alpha
探讨了自然语言处理在量化投资中的应用，从早期关键词情感分析到大语言模型，展示了如何从文本中提取可投资的阿尔法信号，并讨论了数据偏差、前瞻性污染等挑战及解决方案。
Artificial intelligence (AI) and natural language processing (NLP) are opening new frontiers in quantitative investing by transforming unstructured text into systematic, alpha-generating signals. Whereas traditional strategies rely on structured numerical data, most corporate and economic information is communicated in text, making NLP an essential tool for investors. This article explores the evolution of AI and NLP, from early keyword-based sentiment measures to large language models that capture context and meaning. The authors discuss how textual data can be used to extract alpha for investors while emphasizing the pitfalls of working with textual data, including biases, forward-looking contamination, and the opacity of complex models. Case studies demonstrate how robust NLP pipelines—covering translation, validation, and cost management—can deliver investable signals in live portfolios. They highlight operational challenges and solutions that enable scalability across global datasets. Looking ahead, they outline how the growth of alternative data and the evolving toolkit of AI and NLP in extracting investable signals offers new opportunities for quantitative investors who can responsibly harness the AI tools.