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Record W4399365829 · doi:10.3390/jrfm17060234

Can the Presence of Big 4 Auditors in IPO Prospectus Reduce Failure Risk?

2024· article· en· W4399365829 on OpenAlexvenueno aff
Manal Alidarous

Bibliographic record

VenueJournal of risk and financial management · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsProspectusInitial public offeringAuditBusinessAccountingFinance

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.111
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.111
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.005
GPT teacher head0.195
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2024
Admission routes1
Has abstractyes

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