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Fad or future? Automated analysis of financial text and its implications for corporate reporting

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  • Craig Lewis
  • Steven Young

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 is an integral part of company reporting packages, as well as the evolution of text analytic approaches being employed to analyse this content. We provide intuitive descriptions of the leading analytic approaches 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.

Suggested Citation

  • Craig Lewis & Steven Young, 2019. "Fad or future? Automated analysis of financial text and its implications for corporate reporting," Accounting and Business Research, Taylor & Francis Journals, vol. 49(5), pages 587-615, July.
    • Handle: RePEc:taf:acctbr:v:49:y:2019:i:5:p:587-615
    DOI: 10.1080/00014788.2019.1611730
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    Cited by:

    1. Aaryan Gupta & Vinya Dengre & Hamza Abubakar Kheruwala & Manan Shah, 2020. "Comprehensive review of text-mining applications in finance," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-25, December.
    2. Thewissen, James & Shrestha, Prabal & Torsin, Wouter & Pastwa, Anna M., 2022. "Unpacking the black box of ICO white papers: A topic modeling approach," Journal of Corporate Finance, Elsevier, vol. 75(C).
    3. Noha Elberry & Khaled Hussainey, 2021. "Governance Vis-à-Vis Investment Efficiency: Substitutes or Complementary in Their Effects on Disclosure Practice," JRFM, MDPI, vol. 14(1), pages 1-16, January.
    4. Goodell, John W. & Kumar, Satish & Lim, Weng Marc & Pattnaik, Debidutta, 2021. "Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    5. Khaldoon Albitar & Tony Abdoush & Khaled Hussainey, 2023. "Do corporate governance mechanisms and ESG disclosure drive CSR narrative tones?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 3876-3890, October.
    6. Andreas Økland & Nils O. E. Olsson & Marte Venstad, 2021. "Sustainability in Railway Investments, a Study of Early-Phase Analyses and Perceptions," Sustainability, MDPI, vol. 13(2), pages 1-21, January.
    7. Pastwa, Anna M. & Shrestha, Prabal & Thewissen, James & Torsin, Wouter, 2021. "Unpacking the black box of ICO white papers: a topic modeling approach," LIDAM Discussion Papers LFIN 2021018, Université catholique de Louvain, Louvain Finance (LFIN).
    8. Andres Algaba & David Ardia & Keven Bluteau & Samuel Borms & Kris Boudt, 2020. "Econometrics Meets Sentiment: An Overview Of Methodology And Applications," Journal of Economic Surveys, Wiley Blackwell, vol. 34(3), pages 512-547, July.
    9. Berkin, Anil & Aerts, Walter & Van Caneghem, Tom, 2023. "Feasibility analysis of machine learning for performance-related attributional statements," International Journal of Accounting Information Systems, Elsevier, vol. 48(C).

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