Have SFAS 166 and SFAS 167 Improved the Financial Reporting for Securitizations
Bibliographic record
Abstract
Critics have alleged that securitization accounting prior to 2010 was among the causes of the recent financial crisis. In response to this criticism, the Financial Accounting Standards Board (FASB) implemented two new accounting standards, SFAS 166 and SFAS 167, to improve the financial reporting for securitizations. Bank regulators have stated their belief that SFAS 166/167 will result in a consolidated balance sheet (and risk-based capital ratios based thereupon) that better reflects a bank’s exposure to risk related to securitized assets. We document that, by ceding retained power or influence through the servicing / special servicing functions to third parties, SFAS 166/167 resulted in real effects to the extent that banks (particularly those that were weakly capitalized) achieved their accounting objectives in the post-SFAS 166/167 period through legitimate transaction structuring in line with the intent of the new rules. Further, we use capital market participants’ assessments of risk retention by sponsoring banks as a benchmark, and provide evidence consistent with bank regulators’ beliefs. In particular, following SFAS 166/167, equity investors of sponsoring banks do not consider (consider) as risk relevant securitized assets that receive off-balance sheet (on-balance sheet) treatment. Securitized assets that are consolidated under SFAS 166/167 exhibit the same risk relevance as assets that are not securitized, despite contractual provisions that would seem to imply substantial risk transfer.
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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 | 0.045 | 0.106 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".