Using Semantic Fingerprinting in Finance
33 Pages Posted: 28 Mar 2016 Last revised: 3 Aug 2017
Date Written: September 25, 2016
Researchers in finance and adjacent fields have increasingly been working with textual data, a common challenge being analyzing the content of a text. Traditionally, this task has been approached through labor- and computation-intensive work with lists of words. In this paper 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.
Keywords: Textual Analysis, Industries, Stock Returns, Semantic Fingerprint
JEL Classification: G10
Suggested Citation: Suggested Citation