Using Semantic Fingerprinting in Finance

33 Pages Posted: 28 Mar 2016 Last revised: 3 Aug 2017

See all articles by Feriha Ibriyamova

Feriha Ibriyamova

Leiden University

Samuel Kogan

Leiden University - Leiden University College

Galla Salganik-Shoshan

Ben-Gurion University of the Negev

David Stolin

Toulouse Business School - Economics and Finance

Date Written: September 25, 2016

Abstract

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

Ibriyamova, Feriha and Kogan, Samuel and Salganik-Shoshan, Galla and Stolin, David, Using Semantic Fingerprinting in Finance (September 25, 2016). Applied Economics, Vol. 49, No. 28, 2017. Available at SSRN: https://ssrn.com/abstract=2755585 or http://dx.doi.org/10.2139/ssrn.2755585

Feriha Ibriyamova

Leiden University ( email )

Postbus 9500
Leiden, 2300 RA
Netherlands

Samuel Kogan

Leiden University - Leiden University College ( email )

P.O. Box 13228
Den Haag, 2501EE
Netherlands

Galla Salganik-Shoshan

Ben-Gurion University of the Negev ( email )

Beer Sheva
Israel

David Stolin (Contact Author)

Toulouse Business School - Economics and Finance ( email )

20, bd Lascrosses - BP 7010
Toulouse Cedex 7, 31068
France

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