Financial Reporting Regulation and the Reporting of Pro Forma Earnings
Bibliographic record
Abstract
A primary objective of the Sarbanes-Oxley Act is to bolster public confidence in the U.S. capital markets. The SEC aims to achieve this objective in part by regulating the use of alternate earnings measures (colloquially referred to as “pro forma” earnings) that differ from generally accepted accounting principles. This paper examines whether firms change their reporting practice in response to pro forma regulation. Specifically, it examines whether the use, calculation, and presentation of pro forma measures by S&P 500 companies changes between 2001 and 2003. We document three significant shifts in pro forma reporting in this period. First, the proportion of firms reporting pro forma earnings declines from 77 to 54 percent. Second, by 2003, pro forma is used in a less biased manner. Not only is the proportion of firms using pro forma earnings to increase reported income smaller than in 2001, but also the magnitudes of these increases are reduced. Third, in 2003, firms present pro formas in press releases in a much less prominent and less potentially misleading manner. These results suggest a strong impact of the recent regulation of pro forma reporting and provide important empirical evidence for policy makers.
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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 | 0.021 | 0.153 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".