IPO’s Long-Run Performance: Hot Market versus Earnings Management
Bibliographic record
Abstract
One of the IPO-related anomalies that have been well-discussed in the finance literature is the IPO’s long-running underperformance. Two of the major explanations of that phenomenon are: “Hot market” and earnings management. This study investigates the relative importance of these two explanations to the IPO’s long-run underperformance. Our results show that although both hot market and earnings management play a role in explaining IPO’s long-run performance in their own rights, earnings management no longer exhibits significant explanatory power when the IPOs are issued in the cold market. While the IPOs that are issued in the hot market still tend to underperform in the long run even if the firms do not engage in earnings management. Our findings are consistent with the literature related to the information asymmetry in IPO market. And, because the information asymmetry is more severe in hot market condition, IPOs issued in hot market tend to exhibit poorer returns than those issued in cold market.
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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 | 0.001 | 0.006 |
| 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) | 0.004 | 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".