The Association between Auditor Choice, Ownership Retained, and Earnings Disclosure by Firms Making Initial Public Offerings*
Bibliographic record
Abstract
Abstract Using a system of three simultaneous equations, we test the predictions of Datar, Feltham, and Hughes 1991 and Hughes 1986 between auditor choice, earnings disclosures, and retained ownership in U.S. firms making initial public offerings of securities. Using a sample of initial public offerings between 1990 and 1997, we find that the demand for high‐quality auditors increases with firm risk. Additionally, we find that auditor choice, earnings disclosure, and risk are determinants of retained ownership, which is consistent with the predictions of Datar et al. and Hughes that auditor choice and direct disclosure are substitute signals for ownership retention. Further, our results suggest that the signals chosen (i.e., retained ownership, auditor choice, and disclosure) are related through their cost structures and are chosen jointly to minimize the overall cost to the entrepreneur.
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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 | 0.008 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".