Textual Analysis in Finance

38 Pages Posted: 24 Oct 2019 Last revised: 18 Jun 2020

See all articles by Tim Loughran

Tim Loughran

University of Notre Dame

Bill McDonald

University of Notre Dame - Mendoza College of Business - Department of Finance

Date Written: June 17, 2020

Abstract

Textual analysis, implemented at scale, has become an important addition to the methodological toolbox of finance. In this paper, given the proliferation of papers now using this method, we first provide an updated review of the literature while focusing on a few broad topics—social media, political bias, and detecting fraud. While we do not attempt to survey the various methods, we focus on the construction and use of lexicons in finance. We then center the discussion on readability as an attribute frequently incorporated in contemporaneous research, arguing that its use begs the question of what we are measuring. Finally, we discuss how the literature might build on the intent of measuring readability to measure something more appropriate and more broadly relevant—complexity.

Keywords: Textual analysis; complexity; machine learning; readability; Fog Index; social media; lexicons

JEL Classification: D82, D83, G14, G18, G30, M40, M41

Suggested Citation

Loughran, Tim and McDonald, Bill, Textual Analysis in Finance (June 17, 2020). Available at SSRN: https://ssrn.com/abstract=3470272 or http://dx.doi.org/10.2139/ssrn.3470272

Tim Loughran (Contact Author)

University of Notre Dame ( email )

Department of Finance
245 Mendoza College of Business
Notre Dame, IN 46556-5646
United States
574-631-8432 (Phone)
574-631-5255 (Fax)

Bill McDonald

University of Notre Dame - Mendoza College of Business - Department of Finance ( email )

P.O. Box 399
Notre Dame, IN 46556-0399
United States

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