The Voluntary Disclosure of Pro Forma Earnings: A U.S.-Canada Comparison
Bibliographic record
Abstract
This study compares managers' voluntary disclosure of pro forma earnings—an alternative measure to generally accepted accounting principals (GAAP) earnings—in the U.S. and Canada. The results indicate some distinct differences between the two countries in that U.S. managers (1) disclose pro forma earnings more frequently, (2) place greater emphasis on the pro forma earnings number relative to the GAAP earnings figure, and (3) make greater (income-increasing) adjustments from GAAP in calculating pro forma earnings than do their Canadian counterparts. While we find distinct differences in the use of pro forma between the U.S. and Canada, we do not find evidence that it is used for different purposes. Our evidence suggests that in both countries pro forma earnings is used by some corporations to affect users' perceptions of firm performance. Overall, given the differences in managers' use of pro forma, a form of voluntary disclosure, our results suggest caution in moving to a uniform (cross-border) system of financial regulation.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".