(untitled) (gjstudies, 2026)
This research presents a novel, cross-disciplinary methodology for quantifying andinterpreting the informational value of disclosure notes in financial statements, movingbeyond traditional qualitative analysis. While prior literature acknowledges the importance of notes, this study introduces a computational linguistics and network analysisframework to systematically evaluate note complexity, interconnectedness, and predictive power regarding future firm performance. We formulate the problem not as oneof mere compliance or readability, but as an information theory challenge, treating thefull financial statement as a multi-layered data network where notes serve as criticalexplanatory sub-graphs. Our methodology employs text mining techniques to extractthematic clusters, measure semantic density, and construct note-to-statement linkagemaps. Applying this framework to a unique longitudinal dataset of technology andmanufacturing firms from 1998 to 2004, we find that specific structural properties ofdisclosure notes—particularly the centrality of risk and accounting policy explanationswithin the note network—exhibit a significant and previously undocumented correlation with subsequent earnings volatility and analyst forecast dispersion. The resultsdemonstrate that the architecture of disclosure, not just its content, carries materialinformational weight. This represents a fundamental shift from viewing notes as ancillary to recognizing them as an integral, structured component of a firm’s financialcommunication system. Our findings offer original insights for regulators, investors,and auditors, providing a quantitative lens through which the quality and utility offinancial disclosure can be assessed.