Do Syndicated Loan Borrowers Trade-Off Real Activities Manipulation with Accrual-Based Earnings Management?
Bibliographic record
Abstract
This study investigates how managers choose between alternative earnings management mechanisms among syndicated loan borrowers. Specifically, it examines the trade-off between accrual-based earnings management (AEM) and real activities manipulation (RAM) during the period leading up to syndicated loan origination. The study also explores whether lender monitoring mechanisms influence subsequent earnings management behavior. The syndicated loan market, positioned between the private and public fixed income markets, offers a distinctive context for analyzing these strategic decisions. Using a propensity score-matched sample of syndicated and bilateral loans issued between 1989 and 2005, the study finds that firms obtaining syndicated loans are more likely to engage in earnings manipulation beforehand, relying more heavily on AEM than on RAM. Further analysis reveals that monitoring mechanisms—such as lender reputation, the number of syndicate members, loan size, and loan maturity—are significantly associated with future changes in AEM but show a weaker relationship with changes in RAM.
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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 | 0.001 |
| 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 | 0.000 |
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".