Empirical Evidence on Recent Trends in Pro Forma Reporting
Bibliographic record
Abstract
This study provides descriptive evidence on the controversial trend adopted by many firms in recent years of reporting earnings figures on a "pro forma" basis. Pro forma earnings exclude normal income statement items that managers deem to be nonrecurring or nonrepresentative of ongoing operations. We investigate various aspects of pro forma disclosure practice by examining a large sample of actual pro forma press releases issued between January 1998 and December 2000. We find that pro forma announcers tend to be relatively "young" firms that are concentrated primarily in the tech sector and business services industries. We also find that pro forma firms are significantly less profitable, more liquid, and have higher debt levels, P-E ratios, and book-to-market ratios than other firms in their own industries. Our results indicate that while firms commonly exclude multiple expenses in arriving at their pro forma earnings figure, they usually do not exclude the same items in subsequent pro forma announcements. We further find that pro forma announcers' earnings and sales are generally below market averages during our observation period. Interestingly, we also observe that the frequency of pro forma announcements appears to have exploded precisely when earnings and prices of these firms started to decline. Finally, our data provide evidence consistent with the criticism that pro forma announcements may often be motivated by managers' desires to meet or beat analysts' expectations or to avoid earnings decreases.
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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 | 0.004 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".