Textual Analysis in Finance

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