Natural Language Processing for Asset Managers: Turning Text into Alpha

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.