We theoretically develop and empirically apply a new information measure that quantifies the structural dimension of financial statements. Building on the entropy concept from Shannon’s (1948) information theory, our approach captures firm fundamentals directly embedded in accounting numbers and formally measures the information conveyed by their classification structure. The measure is transparent, easy to implement, and adaptable across various classification-based settings. Using balance sheet data for U.S. public firms, we construct firm-quarter entropy-based measures and examine their relationship with market-based proxies that indirectly capture information content. In the first extension, we further demonstrate the measure’s empirical usefulness by applying it to explain financial analysts’ attention allocation decisions. In the second extension, we illustrate the framework’s flexibility by incorporating richer accounting structures using the concept of mutual information and exploring additional empirical applications. Overall, our study offers a theory-based yet practical measure of financial statement classification that complements existing approaches to quantifying accounting information.
| Speaker: | Dr Gaoqing Zhang Heinz Professor of Accounting, Carnegie Mellon University |
| When: |
- |
