Does idiosyncratic risk matter in IPO long-run performance?
Bibliographic record
Abstract
Abstract This paper studies how firm-level idiosyncratic risk varies over time and affects both initial public offering (IPO) and matched non-IPO firms’ long-run performance. It revisits the traditional approach to compute the long-run performance by conditioning aftermarket performance on idiosyncratic risk with a generalized autoregressive conditional heteroskedasticity GARCH-M extension of the standard three-factor Fama and French (3FF) model. Our findings show a positive long-run relationship between idiosyncratic risk and expected returns for almost all IPOs and matched non-IPO firms. We find that, in general, IPOs do not underperform their peers when we adjust long-run abnormal returns for firm-level idiosyncratic risk. We also note that the idiosyncratic risk exposure depends on the IPO profile; it is more important for firms going public in hot-issue markets, undervalued IPOs and high idiosyncratic-risk issues. Thus, this paper suggests that a part of abnormal returns in specific IPOs long-run performance is derived from firm idiosyncratic risk.
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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 | 0.002 | 0.012 |
| 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) | 0.002 | 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".