Asset sales, recourse and investor reactions to initial securitizations
Bibliographic record
Abstract
Purpose Both accounting and regulatory treatments classify securitizations as a “sale” of assets, therefore allowing the issuer to remove the assets from their books. The purpose of this paper is to present conjectural evidence of recourse activity and bankruptcy treatment that undermine the fundamental concept of true sale. Design/methodology/approach The authors use investor reactions to firm’s first securitizations to isolate investors’ views of the potential risk transfer. Findings Investor reactions to firms’ first securitization announcements suggest that investors, themselves, think of the effects of securitizations as more like a financing than an asset sale. Firms securitizing for the first time exhibit negative short-term equity returns and negative long-term operating performance, reactions more similar to financings than asset sales. Additional analysis shows that securitization is also associated with increased systematic risk, suggesting that the rapid growth fueled by securitization is similar to increasing leverage. The effect is more pronounced for banks than non-banks. Originality/value This is the first study to have used firms' first securitizations to analyze the nature of risk transfer in securitizations. The results show that off-balance-sheet treatment for securitizations may be inappropriate, given investor perceptions of the nature of potential contingent liabilities.
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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 | 0.017 |
| 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 | 0.000 | 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".