Do Managers Time Securitization Transactions to Obtain Accounting Benefits?
Bibliographic record
Abstract
ABSTRACT: Relative to recording securitizations as collateralized borrowings, the “gain on sale” treatment has several accounting benefits such as reducing leverage, increasing earnings, and improving efficiency. We investigate whether managers engage in real transaction management to take advantages of these benefits. We predict that in order to maximize financial statement window-dressing, managers will engage in securitizations toward the end of the quarter. We find that 41 percent of the quarter's transactions occur in the third month of the quarter and almost half of these occur in the last five days of the quarter. In addition, we show that when firms report securitization gains sufficient to beat earnings thresholds, the securitization transactions are more likely to have occurred in the last five days of the quarter. We also document that the impact of securitizations on leverage is large and material for many firms. Our results suggest that window-dressing the financial statements appears to be a valuable side-benefit of engaging in securitization transactions.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".