The Effect of Mandatory XBRL Reporting Across the Financial Information Environment: Evidence in the First Waves of Mandated U.S. Filers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study examines the effect of mandatory XBRL disclosure across various aspects of the financial information environment. Our findings show an increase in information efficiency, a decrease in event return volatility, and a reduction of change in stock returns volatility for 428 firms (1,536 10-K and 10-Q filings) post-XBRL disclosure. In addition, this study shows that XBRL mitigates information risk in the market, especially when there is increased uncertainty in the information environment. Our results are robust to various alternative specifications and research modifications such as a matched-pair control (326 XBRL versus 326 non-XBRL firms), current stock market condition, potential earnings releases, and corporate governance. This study contributes to the literature by systematically documenting evidence of how mandatory XBRL disclosure decreases information risk and information asymmetry in both general and uncertain information environments. Our evidence could potentially assist the SEC in their effort to expeditiously assess the benefits of XBRL.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it