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

I examine a fundamental determinant of accounting information: how underlying data are disaggregated. For this, I create a measure of industry disaggregation, which is the extent to which underlying industries map onto segment disclosures. 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. My industry disaggregation measure captures both the disclosure of underlying industries and the avoidance of using non-industry disaggregation criteria. Consistent with analysts' industry specialization and reliance on segment disclosures, I find that industry disaggregation is negatively associated with analyst forecast error and dispersion, and positively associated with analyst following and intra-industry information transfers among analysts. Collectively, these findings indicate that financial information is more informative when disaggregated by standardized criteria that achieve comparability and match the information processing strategies of capital market participants