Driving Venture Capital Interest: The Influence of the Big 4 Audit Firms on IPOs
Bibliographic record
Abstract
This paper investigated how hiring one of the Big 4 auditing firms helps initial public offering (IPO) owners attract venture capitalists’ (VCs) backing when going public to address the gap in auditing and venture capital literature. For this, the paper examined a large dataset from 1995 to 2019 consisting of 33,536 IPO firms from 22 countries with diverse socioeconomic, political, and cultural contexts. The study found that hiring Big 4 auditors increases IPO owners’ chances of recruiting VCs by up to 50%. The analysis also supports prior findings, which state that IPO owners strategically choose Big 4 audit firms to lower agency costs and send quality signals to improve openness and disclosure as well as boost VCs’ confidence in the IPO market. This research offers multiple benefits to academics, policymakers, investors, and issuers.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".