Are Securitizations in Substance Sales or Secured Borrowings? Capital‐Market Evidence*
Bibliographic record
Abstract
Abstract Two standard‐setting approaches have emerged globally to guide the choice of accounting for securitizations: the control and components approach (SFAS No. 125 and SFAS No. 140) and the risks and rewards transfer approach (IAS No. 39). A lack of consensus about derecognition accounting is a major impediment to achieving convergence in global standards that must be resolved. Thus, both SFAS No. 140 and IAS No. 39 will be reexamined, and evidence pertinent to the debate is timely and important. In this study, we present evidence consistent with the view of credit‐rating analysts, who view many securitizations as, in substance, secured borrowings. Specifically, for a sample of originators applying sale accounting guidance in SFAS No. 125 / 140 during the period 1997‐2003, we show that off‐balance‐sheet debt related to securitizations has, on average, the same risk‐relevance for explaining market measures of risk (that is, CAPM beta) as on‐balance‐sheet debt. We also find that, in a returns and earnings association framework, the pricing multiple on securitization gains declines as the amount of off‐balance‐sheet debt increases, implying that investors take off‐balance‐sheet debt into account when assessing the valuation‐relevance of such gains. For those who advocate the control and components approach to securitization accounting, our results suggest that, at least for frequent securitizers, the put option arising from implicit recourse is a “missing piece” that is not currently accounted for when calculating securitization gains. Our results challenge the extant measurement standards in SFAS No. 140.
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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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".