Mobilizing Text As Data

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.