Accrual vs. Real Earnings Management in Internationally Diversified Firms: The Role of Institutional Supervision
Bibliographic record
Abstract
This study investigates whether internationally diversified firms substitute between accrual-based and real earnings management and examines how institutional supervision moderates this relationship. Drawing on a sample of Taiwanese firms listed on the Taiwan Stock Exchange from 2003 to 2016, we conduct regression analyses to test our hypothesis. We find that internationally diversified firms actively shift between accrual and real earnings management strategies depending on the constraints they face. Specifically, firms tend to rely more on accrual-based manipulation when information asymmetry is high and switch to real earnings management when accruals are more easily detected. We also show that stronger institutional supervision—measured by information transparency and investor protection—significantly curbs accrual-based earnings management. These findings reflect the higher volatility and agency problems associated with international operations, such as exposure to foreign risks and the distance between parent and subsidiary firms. By highlighting the conditions under which firms manage earnings and the supervisory mechanisms that constrain such behavior, this study offers practical insights for managers seeking to smooth earnings, investors aiming to evaluate firm transparency, and policymakers designing regulations to deter opportunistic financial reporting.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".