Information Precision and Long‐Run Performance of Initial Public Offerings
Bibliographic record
Abstract
Due to a lack of an information history, IPO firms' information precision is not only generally low but also likely to be estimated initially with considerable error. I hypothesize and find that the deviation between expected and realized information precision is predictably associated with the magnitude and the persistence of long‐run abnormal returns after an IPO. Specifically, an upward (downward) revision of information precision results in positive (negative) abnormal returns over the period in which investors update their beliefs. In addition, the positive abnormal returns of firms with unexpectedly high realized information precision are less persistent than the negative abnormal returns of firms with unexpectedly low realized information precision, which can extend up to 18 months after the IPO. The findings imply that long‐term investors in IPO stocks do not necessarily behave irrationally, but that both positive and negative post‐IPO abnormal performance is also consistent with rational investors gradually updating the perceived information precision parameter of these stocks.
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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 | 0.042 |
| 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 | 0.003 | 0.002 |
| 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".