Can the Presence of Big 4 Auditors in IPO Prospectus Reduce Failure Risk?
Bibliographic record
Abstract
This paper addresses a void in the research on auditing and initial public offering (IPO) failure by investigating the impact of the Big 4 auditing firms on the likelihood of an IPO failure. This research is the first comprehensive analysis of more than 33,000 global IPOs that either failed or were successful between 1995 and 2019 across a wide range of nations with vastly different regulatory, cultural, and economic settings. A cross-sectional probit regression model is utilized to investigate the influence of hiring the Big 4 auditing firms on IPO failure, building upon prior studies on IPO failure. We found strong evidence that IPO failure rates were diminished by up to 67% when one of the Big 4 auditing firms was involved in auditing the IPO prospectus. For IPO founders, hiring Big 4 auditors before an IPO is a quality signaling strategy that minimizes the risk of a failed IPO by reducing information asymmetry among IPO participants. Our findings provide useful policy implications. Hiring one of the Big 4 auditing firms before an IPO is a reassuring signaling strategy for founders, since it decreases information asymmetry among IPO investors and so lowers the risk of the IPO failing. Primary market investors now have access to credible evidence indicating that backing IPOs from companies that use the Big 4 auditing firms increases the likelihood of such IPOs being listed on stock exchanges and yields positive returns. This is the first time, as far as the academicians are aware, that conclusive evidence has been found of a strong inverse association between the presence of Big 4 audits and failure risk for IPO firms. Our research could be helpful to primary market regulators since it shows how crucial it is to encourage Big 4 audits in IPO companies. The quality work of the Big 4 auditors does lower the risk of failure in the IPO market, which might help owners of small private equities to list their firms on the IPO market, boosting economic growth.
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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 | 0.012 | 0.111 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".