The Informational Value of Segment Data Disaggregated by Underlying Industry: Evidence from the Textual Features of Business Descriptions

• 成果类型: Article
• 署名作者: Song, Shiwon
• 署名单位: INSEAD Business School
• 刊物名称: ACCOUNTING REVIEW
• ISSN/ISSBN: 0001-4826
• DOI: 10.2308/TAR-2017-0572
• 发表日期: 2021
• 页码: 361-396
• 关键词: discretionary disclosure; performance; accuracy; analysts; earnings; MARKET; QUALITY; ability; IMPACT

摘要

I examine a fundamental determinant of disclosure quality: how underlying data are disaggregated. For this, I create a measure of industry disaggregation, which is the extent to which segment disclosures are disaggregated based on underlying industries. To identify underlying industries, I apply a deep learning algorithm that extracts textual features from Item 1 business descriptions, in which firms are required to accurately describe their products and services. Industry disaggregation captures the disclosure of underlying industries and the adherence to industry-based disaggregation criteria. Consistent with capital markets being informationally segmented by industry, I find that industry disaggregation is negatively associated with analyst forecast error and dispersion, and positively associated with analyst following and information transfers among analysts and investors. These findings indicate that financial information is more informative, and, thus, of higher quality, when disaggregated by standardized criteria that achieve comparability and match the information-processing strategies of capital market participants.