In Search of Meaning: Lessons, Resources and Next Steps for Computational Analysis of Financial Discourse
This is an incremental review that identifies gaps and opportunities for researchers in accounting and finance to improve computational analysis of financial discourse.
The paper critically assesses the application of computational linguistics methods in accounting and finance research, finding that the field lags behind in adopting advanced techniques and that implementation issues limit the practical benefits, while highlighting four promising tools like named entity recognition for future enhancement.
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.