Disclosure Overload? A Professional User Perspective on the Usefulness of General Purpose Financial Statements
Bibliographic record
Abstract
ABSTRACT We survey a broad group of professionals who use financial statements as part of their job to assess the extent to which they believe financial reports suffer from disclosure overload. Consistent with the claims made by regulators, auditors, and preparers, we find that a significant portion of professional financial statement users believe disclosure overload is a problem. However, this group is in the minority, with about twice as many professional users believing that overload is not a problem and that more information should be disclosed in financial statements. This dichotomy presents a difficult challenge to standard setters aiming to improve financial reporting by altering the amount of information provided in financial reports. To that end, we complement existing research on the informativeness of accounting information by measuring perceptions of the usefulness of the various financial statements and their footnotes across a variety of tasks. Finally, we develop a framework that could be useful in developing a theory of disclosure overload.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".