Using semantic fingerprinting in finance

Researchers in finance and adjacent fields have increasingly been working with textual data, a common challenge being analysing the content of a text. Traditionally, this task has been approached through labour- and computation-intensive work with lists of words. In this article we compare word list analysis with an easy-to-implement and computationally efficient alternative called semantic fingerprinting. Using the prediction of stock return correlations as an illustration, we show semantic fingerprinting to produce superior results. We argue that semantic fingerprinting significantly reduces the barrier to entry for research involving textual content analysis, and we provide guidance on implementing this technique.