How Do Auditors Behave During Periods of Market Euphoria? The Case of Internet IPOs*
Bibliographic record
Abstract
How do auditors behave during periods of market euphoria? To address this question, we study auditor going-concern opinions around the time of the wave of stressed Internet companies filing to go public on Nasdaq, a period many characterize as the ‘dot com bubble’. We focus on the day the auditor signs the opinion that appears in a stressed, Internet registrants’ IPO filing and document a sharp increase in the number of opinions with dates between January 1999 and April 2000. Contemporaneous with this jump in transaction volume, and for the duration of these 16-months, Big 5 firms were less likely to render going-concern opinions to their stressed, Internet IPO registrant clients. Upon conducting tests for determinants that could lead auditors to shift their decision criteria during this euphoric audit market, we find the presence of a going-concern opinion varies with variables that proxy for client reasons (financial distress, company age, venture backing, IPO cash burn) and for less auditor independence/skepticism (recent fees for clients without venture backing and a rush-to-market for clients with venture backing) by the Big 5 firms. These findings suggest a mixed conclusion regarding the Big 5's behavior; as the presence of a going-concern opinion varies inversely with variables that proxy for both client viability and auditor self interest. As for consequences to investors, our analysis of two, three and four-year post-IPO stock delisting provides some evidence of a decrease in the predictive content (early-warning value) of Big 5 opinions signed during the Internet IPO bubble.
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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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".