The Effect of Mandatory XBRL Reporting Across the Financial Information Environment: Evidence in the First Waves of Mandated U.S. Filers
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".