Valuing Initial Public Offerings Using Article 11 Pro Forma Financial Information in the Prospectus*
Bibliographic record
Abstract
ABSTRACT We investigate whether Article 11 pro forma financial information assists investors in valuing IPOs. While the SEC expects it to be helpful in assisting investment decisions, Article 11 pro forma financial information is based on registrants' understanding and assumptions, and registrants can exercise their own judgment when preparing pro forma financial statements. It is therefore an empirical question whether the information contained in pro forma financial statements is useful to investors. We examine the association between pro forma adjustments of earnings and book value of equity and the IPO offer value and find asymmetric results. While positive pro forma adjustments of earnings and book value of equity are positively associated with the IPO offer value, negative pro forma adjustments of earnings and book value of equity are negatively associated with the IPO offer value, suggesting that negative pro forma adjustments are priced as growth opportunities. Additional analyses reveal that the association between pro forma adjustments of book value of equity and the IPO offer value varies across different time periods and industries and that pro forma adjustments of book value of equity are initially mispriced by investors. In contrast, we do not find similar results for pro forma adjustments of earnings. Further empirical tests show that the asymmetric results of mispricing of pro forma adjustments of earnings and book value of equity may be explained by the requirements of Article 11 of Regulation S‐X for pro forma adjustments dictating that adjustments to earnings reflect only recurring items while adjustments to book value reflect both recurring and nonrecurring items.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 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".