Examining the Role of Auditor Quality and Retained Ownership in IPO Markets: Experimental Evidence*
Bibliographic record
Abstract
Abstract We use experimental markets to test the Datar, Feltham, and Hughes (DFH) 1991 model of entrepreneur choice of auditor and retained ownership in initial public offerings (IPOs). DFH predict that entrepreneurs use retained ownership to signal IPO value and substitute high‐quality auditors for retained ownership to signal value as the risk of the IPO increases. Given the mixed support for DFH from archival research, we conduct experimental markets that directly operationalize the model's decision variables, which permits a direct test of whether the model is descriptively valid. In addition, our market setting provides a strong test of this theory by including an alternative Nash equilibrium also present in field settings, one in which only auditor quality is used by entrepreneurs to signal IPO value. Our results suggest that DFH predict entrepreneur behavior in baseline markets where both computerized investors and auditors are programmed to price consistently with the DFH equilibrium. However, the DFH model does not describe behavior when “robot” investors are replaced with human investors in the market. The results suggest that entrepreneurs and investors strategically interact in a manner that leads them away from the DFH equilibrium and toward the alternative Nash equilibrium behavior of entrepreneurs with high‐value assets hiring high‐quality auditors irrespective of IPO risk. Our results imply that the DFH model has limited descriptive validity, document the importance of strategic behavior on market equilibrium formation, and suggest that the mixed results found in prior DFH‐based field studies may reflect the model's low descriptive validity.
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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 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".