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  • Lewis_Young_PD_Leake_Lecture_Nov2018_v12_final

    Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in Accounting and Business Research on 01 Jun 2019, available online: https://www.tandfonline.com/doi/full/10.1080/00014788.2019.1611730

    Accepted author manuscript, 1.35 MB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

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Fad or Future?: Automated Analysis of Financial Text and its Implications for Corporate Reporting

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Published
<mark>Journal publication date</mark>29/05/2019
<mark>Journal</mark>Accounting and Business Research
Issue number5
Volume49
Number of pages29
Pages (from-to)587-615
Publication StatusPublished
<mark>Original language</mark>English

Abstract

This paper describes the current state of natural language processing (NLP) as it applies to corporate reporting. We document dramatic increases in the quantity of verbal content that are an integral part of company reporting packages as well as the evolution of text analytic approaches that are being employed to analyse this content. We provide intuitive descriptions of the leading analytic approaches that are applied in the academic accounting and finance literatures. This discussion includes key word searches and counts, attribute dictionaries, naïve Bayesian classification, cosine similarity, and latent Dirichlet allocation. We also discuss how increasing interest in NLP processing of the corporate reporting package could and should influence financial reporting regulation and note that textual analysis is currently more of an afterthought, if it is even considered. Opportunities for improving the usefulness of NLP processing are discussed as well as possible impediments.

Bibliographic note

This is an Accepted Manuscript of an article published by Taylor & Francis in Accounting and Business Research on 01 Jun 2019, available online: https://www.tandfonline.com/doi/full/10.1080/00014788.2019.1611730