Financial researchers have been turning words into quantifiable data for almost two decades. In a 2007 Journal of Finance study, Paul Tetlock quantified pessimism in a Wall Street Journal column and demonstrated its relationship with market prices and trading volume. A year later, Tetlock and colleagues extended the approach to company-specific news, finding that negative language helped forecast earnings while prices initially underreacted to the information it contained.
Yet extracting financial meaning from language was not straightforward. In 2011, Tim Loughran and Bill McDonald showed that almost three-quarters of the words labelled “negative” by a widely used general-purpose dictionary were not normally negative in financial contexts. A “liability” is not necessarily bad news simply because every day English makes it sound ominous. Their finance-specific dictionaries improved the measurement, but the broader lesson was more important: a method could process language consistently while still misunderstanding what that language meant economically.
Meanwhile, computational linguistics was developing new ways to represent language itself. Word2Vec and related models made it possible to compare words with related meanings even when they did not literally overlap. The significance was not that computers had suddenly discovered meaning, but that relationships between concepts could increasingly be quantified. This helped transform search, allowing systems to identify relevant information based on meaning rather than exact keywords.
For finance, these advances opened the possibility of understanding not just which words appeared in a document, but what they referred to and how they related to one another. Aspect-based sentiment analysis, for example, could distinguish optimism about a company’s sales from concern about its margins, while contextual language models made representations sensitive to surrounding text. Financial-domain models such as FinBERT brought these developments back to Loughran and McDonald’s original insight: understanding language in general is not quite the same as understanding it in finance – context matters.
