Audit Committee, Underpricing of IPOs, and Accuracy of Management Earnings Forecasts
Bibliographic record
Abstract
ABSTRACT Manuscript Type: Empirical Research Question/Issue: This paper examines the role of audit committees (AC) in the initial public offering (IPO) process in a governance environment where AC best practices are well established but their adoption is voluntary. We consider the creation and characteristics of the committee as signals that issuing firms can use to reduce the underpricing often associated with IPOs. We also examine the effect of the committee on the quality of management earnings forecasts included in the prospectus. Research Findings/Results: Our empirical analysis is performed on a sample of 246 IPOs issued in the Canadian province of Québec. We find that the creation of an AC at the time of the IPO has no effect on underpricing unless its members are independent and have expertise in financial matters, in which case it decreases significantly the level of underpricing of the IPO. However, we find no significant association between these two governance attributes and the accuracy of forecasts included in prospectuses. Theoretical Implications: Our results suggest that the AC is a credible signal that could be used in the firm's signaling strategy and the results provide support for the monitoring role of the board of directors, as proposed by the agency theory. Practical/Policy Implications: Our results support the worldwide movement in legislations requiring AC independence and expertise. They stress the importance of the presence of qualified members on the AC with sufficient knowledge of accounting and finance.
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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 | 0.008 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".