The Credibility of Earnings Forecasts in IPO Prospectuses and Underpricing
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it