Earnings Management Prior to Initial Public Offerings and Its Effect on Firm Performance: International Evidence
Bibliographic record
Abstract
This paper investigates the degree of discretionary current accruals (DCA-1) prior to the initial public offerings (IPOs) of foreign firms in an attempt to study the two seemingly opposing views of Teoh, Welch, and Wong (1998) and Ball and Shivakumar (2008) in regards to pre-IPO earnings management. By analyzing a sample of 4962 IPOs from 28 countries, I find that, on average, IPO firms do not report significantly positive DCA-1. This result supports the view held by Ball and Shivakumar that IPO firms do not engage in earnings management and it is inconsistent with the earnings management hypothesis of Teoh et al. (1998). Furthermore, results support the criticism of Ball and Shivakumar (2008) that the use of discretionary accruals in the IPO year (DCA0) is a biased measure of earnings management. However, consistent with the hypothesis of Teoh et al. (1998), results show that firms with higher discretionary accruals (DCA-1 or DCA0) underperform in the long run. The negative relationship between the long-term performance and the level of DCA is robust to several measures of long-term performance (cumulative abnormal returns-CAR, buy-and-hold abnormal returns-BHAR, Fama-French 4-factor model-Alpha), to several time horizons (3 and 5 years), and holds even after controlling for several firm characteristics. Overall, the results show that although on average IPO firms don’t engage in earnings management, the ones that do, underperform in the long run.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".