Founder Succession and Accounting Properties*
Bibliographic record
Abstract
Using a sample of 231 firms in Hong Kong, Singapore, and Taiwan, we examine the changes in firms' accounting practice around leadership successions-the turnovers of chairmen. We find that the successions are associated with reduction in the firms' unsigned discretionary accruals and an increase in timely loss recognition. We argue that the incumbent chairmen cannot transfer most of their unique assets, such as reputation and political/social networks, to their successors, resulting in reduced relationship-based contracting post succession. This change from relationship-based to market-based contracting also shifts the firms to a less insider-based accounting system. Moreover, we find that the extent of the shift in accounting is larger in founder successions ( turnovers of chairmen that are founders) than in subsequent ( non-founder) successions, possibly because the loss of unique assets is more pronounced in the first succession than in subsequent successions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".