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In search of meaning: Lessons, resources and next steps for computational analysis of financial discourse

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  • Mahmoud El‐Haj
  • Paul Rayson
  • Martin Walker
  • Steven Young
  • Vasiliki Simaki

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 studies 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.

Suggested Citation

  • Mahmoud El‐Haj & Paul Rayson & Martin Walker & Steven Young & Vasiliki Simaki, 2019. "In search of meaning: Lessons, resources and next steps for computational analysis of financial discourse," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 46(3-4), pages 265-306, March.
    • Handle: RePEc:bla:jbfnac:v:46:y:2019:i:3-4:p:265-306
    DOI: 10.1111/jbfa.12378
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    5. 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).
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    9. 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).
    10. Marcus Buckmann & Andy Haldane & Anne-Caroline Hüser, 2021. "Comparing minds and machines: implications for financial stability," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 37(3), pages 479-508.
    11. Hertel, Tobias & Kaya, Devrimi & Reichmann, Doron, 2024. "Corporate culture and M&A deals: Using text from crowdsourced employer reviews to measure cultural differences," Journal of Banking & Finance, Elsevier, vol. 161(C).
    12. Stefania Veltri & Francesco De Luca & Ho‐Tan‐Phat Phan, 2020. "Do investors value companies' mandatory nonfinancial risk disclosure? An empirical analysis of the Italian context after the EU Directive," Business Strategy and the Environment, Wiley Blackwell, vol. 29(6), pages 2226-2237, September.
    13. Ahmad A. Toumeh, 2024. "Assessing the potential integration of large language models in accounting practices: evidence from an emerging economy," Future Business Journal, Springer, vol. 10(1), pages 1-15, December.
    14. Söhnke M. Bartram & Harald Lohre & Peter F. Pope & Ananthalakshmi Ranganathan, 2021. "Navigating the factor zoo around the world: an institutional investor perspective," Journal of Business Economics, Springer, vol. 91(5), pages 655-703, July.
    15. Ho-Tan-Phat Phan & Francesco De Luca & Lea Iaia, 2020. "The “Walk” towards the UN Sustainable Development Goals: Does Mandated “Talk” through NonFinancial Disclosure Affect Companies’ Financial Performance?," Sustainability, MDPI, vol. 12(6), pages 1-20, March.
    16. Andrew Todd & James Bowden & Yashar Moshfeghi, 2024. "Text‐based sentiment analysis in finance: Synthesising the existing literature and exploring future directions," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(1), March.
    17. Ankit Jain & Hariom Manchiraju & Shyam V. Sunder, 2023. "Institutional ownership and the informativeness of disclosure tone," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 50(1-2), pages 61-90, January.
    18. Steven F. Cahan & Seokjoo Chang & Wei Z. Siqueira & Kinsun Tam, 2022. "The roles of XBRL and processed XBRL in 10‐K readability," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 49(1-2), pages 33-68, January.
    19. 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).
    20. Murat Aydogdu & Hakan Saraoglu & David Louton, 2019. "Using long short‐term memory neural networks to analyze SEC 13D filings: A recipe for human and machine interaction," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 26(4), pages 153-163, October.
    21. Arslan-Ayaydin, Özgür & Bishara, Norman & Thewissen, James & Torsin, Wouter, 2020. "Managerial career concerns and the content of corporate disclosures: An analysis of the tone of earnings press releases," International Review of Financial Analysis, Elsevier, vol. 72(C).
    22. Mehmet Yildiz & Nicola Pless & Semih Ceyhan & Rob Hallak, 2023. "Responsible Leadership and Innovation during COVID-19: Evidence from the Australian Tourism and Hospitality Sector," Sustainability, MDPI, vol. 15(6), pages 1-21, March.
    23. Moreno, Angel-Ivan & Caminero, Teresa, 2022. "Application of text mining to the analysis of climate-related disclosures," International Review of Financial Analysis, Elsevier, vol. 83(C).
    24. Bassyouny, Hesham & Abdelfattah, Tarek & Tao, Lei, 2022. "Narrative disclosure tone: A review and areas for future research," Journal of International Accounting, Auditing and Taxation, Elsevier, vol. 49(C).

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