Largest Shareholder’s Participation in Management and Quarterly Earnings Quality
Bibliographic record
Abstract
It has been considered that the higher the stake of the largest shareholder, the higher the possibility of a company’s opportunistic behavior. However, the prior studies have not succeeded in showing consistent results between the largest shareholder’s stake and accounting numbers. Therefore, this study empirically tried to examine the effect of the largest shareholder’s share on quarterly earnings quality depending on whether the largest shareholder directly manages the business or not. Quarterly accruals quality was measured based on the Francis et al. (2005) model and then, it was used as a proxy for quarterly earnings quality. As a result of analyzing 14,060 company-year-quarter data of KRX-listed companies from 2015 to 2021, it showed that the higher the share ratio of the largest shareholder, the quarterly earnings quality would deteriorate when the largest shareholder participates in management. Additional analysis reflecting the non-linear relation of the largest shareholder’s ratio to the accounting numbers also showed that the quarterly earnings quality was more affected when the largest shareholder directly participated in management. In addition, it was confirmed that the largest shareholder’s opportunistic behavior was more prominent in the fourth quarter when earnings management motives were expected to be higher than in other quarters.
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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 | 0.002 | 0.008 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".