In Search of Meaning: Lessons, Resources and Next Steps for Computational Analysis of Financial Discourse

TL;DR

This paper critically reviews the use of computational linguistics in financial discourse analysis, highlighting gaps, challenges, and promising tools like NER, summarization, semantics, and corpus linguistics for future research.

Contribution

It identifies the lag in CL methods application in finance, discusses implementation challenges, and proposes four promising CL tools to advance financial discourse analysis.

Findings

Finance research lags in CL method adoption

Implementation issues reduce CL benefits

Structural limitations hinder practical relevance

Abstract

We critically assess mainstream accounting and finance research applying methods from computational linguistics (CL) to study financial discourse. We also review common themes and innovations in the literature and assess the incremental contributions of work applying CL methods over manual content analysis. Key conclusions emerging from our analysis are: (a) accounting and finance research is behind the curve in terms of CL methods generally and word sense disambiguation in particular; (b) implementation issues mean the proposed benefits of CL are often less pronounced than proponents suggest; (c) structural issues limit practical relevance; and (d) CL methods and high quality manual analysis represent complementary approaches to analyzing financial discourse. We describe four CL tools that have yet to gain traction in mainstream AF research but which we believe offer promising ways to enhance the study of meaning in financial discourse. The four tools are named entity recognition (NER), summarization, semantics and corpus linguistics.