Ownership Structure, Business Group Affiliation, Listing Status, and Earnings Management: Evidence from Korea*
Bibliographic record
Abstract
Abstract Using a large sample of both publicly traded and privately held firms in South Korea (hereafter “Korea”), we investigate whether, and how, the deviation of controlling shareholders' control from ownership, business group affiliation, and listing status differentially affect the extent of earnings management. Our study yields three major findings. First, we find that as the control‐ownership disparity becomes larger, controlling shareholders tend to engage more in opportunistic earnings management to hide their behavior and avoid adverse consequences such as disciplinary action. The result of our full‐model regression reveals that an increase in the control‐ownership wedge by 1 percent leads to an increase in the magnitude of (unsigned) discretionary accruals by 1.3 percent of lagged total assets, ceteris paribus. Second, we find that for our full‐model regression, the magnitude of (unsigned) discretionary accruals is greater for group‐affiliated firms than for nonaffiliated firms by 0.8 percent of lagged total assets. This result suggests that business group affiliation provides controlling shareholders with more incentives and opportunities for earnings management. Finally, we find that for our full‐model regression, the magnitude of (unsigned) discretionary accruals is greater for publicly traded firms than for privately held firms by 1.2 percent of lagged total assets. This result supports the notion that stock markets create incentives for public firms to manage reported earnings to satisfy the expectations of various market participants that are often expressed in earnings numbers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".