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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".