Bibliographic record
Abstract
ABSTRACT Motivated by ongoing worldwide efforts to improve the comparability of accounting information, I examine the temporal trend in global financial reporting comparability. Regulators have made serious efforts to improve comparability, but numerous frictions may have limited their effectiveness. Accordingly, I examine the time‐series properties of comparability measures for a sample of the 36 largest economies and provide two key empirical insights consistent with expectations. First, I confirm comparability is increasing over 2002–2018. Second, I document that this increase primarily occurs in firms applying local accounting standards, as opposed to those applying global standards (defined as either US GAAP or IFRS). Additional analyses reveal that: (i) firms applying local standards are becoming more comparable to firms applying IFRS but not to those applying US GAAP, and (ii) comparability within global‐standards firms is not changing. I also document that certain market liquidity benefits of comparability are sustained in the long term, as firms increasing comparability over the sample period experience greater reductions in bid‐ask spread and zero‐return trading days relative to those decreasing comparability. Overall, the results reveal that comparability has increased—consistent with systematic regulatory efforts—but that this increase arises heterogeneously across firms, with the primary effects in recent years occurring among those applying local standards.
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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 | 0.030 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".