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Faculty & Research


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

Journal Article
The author examines a fundamental determinant of disclosure quality: how underlying data are disaggregated. For this, the author creates a measure of industry disaggregation, which is the extent to which segment disclosures are disaggregated based on underlying industries. To identify underlying industries, the author applies 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, the author finds 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.

Assistant Professor of Accounting and Control