Voluntary Disclosure of Management Earnings Forecasts in IPO Prospectuses
Bibliographic record
Abstract
Asymmetric information and mechanisms for its resolution in the initial public offering (IPO) process are subjects of extensive research and debate. In this paper, we investigate the impact of one such mechanism, namely voluntary disclosure of management earnings forecasts by issuers of IPOs, as a means of reducing asymmetric information as well as ex ante uncertainty. Our focus is on the relative importance of this voluntary disclosure mechanism on both IPO underpricing and post-issue return performance. Our results indicate that management earnings forecasts provide important and incremental information compared to other means of reducing asymmetric information, and these disclosures appear to improve the environment of IPO issuance. For example, our underpricing results show that firms that choose to provide forecasts leave less money on the table with a lower degree of underpricing. In terms of post-issue performance, firms whose forecasts turn out to be optimistic are penalized significantly relative to other forecasters and non-forecasters.
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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 | 0.047 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".