Mobilizing Text As Data

Jihun Bae, Laurence van Lent, Chung-Yu Hung*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

9 Citations (Scopus)
14 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)1085-1106
Number of pages22
JournalEuropean Accounting Review
Volume32
Issue number5
DOIs
Publication statusPublished - 2023

Bibliographical note

Publisher Copyright:
© 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

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