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
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Fad or Future?
T2 - Automated Analysis of Financial Text and its Implications for Corporate Reporting
AU - Lewis, Craig
AU - Young, Steven
N1 - 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
PY - 2019/5/29
Y1 - 2019/5/29
N2 - 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.
AB - 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.
U2 - 10.1080/00014788.2019.1611730
DO - 10.1080/00014788.2019.1611730
M3 - Journal article
VL - 49
SP - 587
EP - 615
JO - Accounting and Business Research
JF - Accounting and Business Research
SN - 0001-4788
IS - 5
ER -