When Does Pre‐<scp>IPO</scp> Financial Reporting Trigger Post‐<scp>IPO</scp> Legal Consequences?
Bibliographic record
Abstract
Abstract Prior research suggests that the fear of litigation precludes most managers from manipulating earnings in the initial public offering (IPO) setting. Yet, managers' restraint is perhaps unwarranted: research has not yet linked instances of aggressive pre‐IPO reporting to increased litigation risk. This paper investigates when aggressive IPO reporting triggers legal consequences. Examining 2,037 IPOs, we find that even when ex post evidence indicates the presence of earnings inflation, litigation is more likely to occur when investors have relied on the suspect earnings during the pricing process. Why might investors rely on some firms' abnormal accruals when valuing the IPO and yet discount the abnormal accruals of other firms? Our analyses suggest that IPO investors incorporate abnormal accrual information into IPO prices in situations where accruals are more likely to reflect information and where other sources of information to help investors make pricing decisions are lacking or are less reliable. In these situations, we find that abnormal accruals do positively correlate with future performance, validating investors' use of this information when pricing these offerings. Yet, when ex post performance reveals that these pre‐IPO abnormal accruals were in fact inflated, we find that litigation emerges to allow harmed shareholders to recover losses incurred dating back to the pricing process—importantly, investors are only harmed if they used those abnormal accruals in pricing the IPO. Collectively, our evidence indicates that litigation in response to earnings inflation does indeed surface in the IPO setting—but only when investors need it to settle the score.
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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 | 0.004 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".