The Credibility of Earnings Forecasts in IPO Prospectuses and Underpricing
Bibliographic record
Abstract
This paper provides empirical evidence of the impact of the voluntary disclosure of management earnings forecasts in IPO prospectuses and of the credibility of these forecasts, as perceived by investors at the time of the IPO. We measure forecast credibility ex ante with two approaches: (i) a vector of determinants of credibility that are observable by market participants at the time of the issue and (ii) the predicted value of the forecast error based on some of these determinants. Controlling for the firm's decision on whether or not to issue a forecast, we find that the issue of a forecast reduces underpricing. We find that the quality of the firm's governance and of the auditor and underwriter associated with the issue seems to act as a substitute to the disclosure of an earnings forecast in the prospectus, so that they significantly decrease the level of underpricing only for non-forecasters. However, despite our various approaches to measure ex ante credibility, we find no association between the pricing of the issue and perceived forecast credibility at the time of the IPO.
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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 | 0.006 | 0.107 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".