Does Investment Bankers’ Prior Experience in Public Accounting Reduce Earnings Management in IPO Firms?
Bibliographic record
Abstract
SUMMARY We examine whether firms undertaking an initial public offering (IPO) exhibit less earnings management when individual investment bankers have prior experience in public accounting. Although auditors are primarily responsible for providing external monitoring of the financial reporting process, individual bankers also have strong incentives to improve accounting quality in firms going public. We predict a negative relation between public accounting experience and IPO firms’ earnings management because working in public practice fosters individual bankers’ accounting expertise and conservative personalities. In exploiting unique disclosures of investment bankers’ identities and characteristics in China, our analysis indicates that bankers with early-career public accounting experience constrain IPO firms’ accrual-based earnings management. Consistent with expectations, we find that this evidence is more pronounced if the accounting firm that employed the future investment banker is larger, is permitted to audit listed companies, and was previously subject to a regulatory sanction. Data Availability: Most data are publicly available from the sources identified in the paper. Hand-collected data from the Securities Association of China (SAC) can be made available upon request. JEL Classifications: G24; M41; M42.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".