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Mobilizing Text As Data

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  • Jihun Bae
  • Chung Yu Hung
  • Laurence van Lent

Abstract

Textual analysis methods have become increasingly popular and powerful tools for researchers in finance and accounting to extract meaningful information from unstructured text data. This paper surveys the recent applications of these methods in various domains, such as corporate disclosures, earnings calls, investor relations, and social media. It also discusses the advantages and challenges of different textual analysis methods, such as keyword lists, pattern-based sequence classification, word embedding, and other large language models. We provide guidance on how to choose appropriate methods, validate text-based measures, and report text-based evidence effectively. We conclude by suggesting some promising directions for future research using text as data.

Suggested Citation

  • Jihun Bae & Chung Yu Hung & Laurence van Lent, 2023. "Mobilizing Text As Data," European Accounting Review, Taylor & Francis Journals, vol. 32(5), pages 1085-1106, October.
    • Handle: RePEc:taf:euract:v:32:y:2023:i:5:p:1085-1106
    DOI: 10.1080/09638180.2023.2218423
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    Cited by:

    1. Tarek A. Hassan & Stephan Hollander & Aakash Kalyani & Markus Schwedeler & Ahmed Tahoun & Laurence van Lent, 2024. "Economic Surveillance using Corporate Text," Working Papers 2024-022, Federal Reserve Bank of St. Louis.

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