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Record W3009285094 · doi:10.1111/jbfa.12449

Have SFAS 166 and SFAS 167 improved the financial reporting for securitizations?

2020· article· en· W3009285094 on OpenAlexafffund
Minkwan Ahn, Samuel B. Bonsall, Zahn Bozanic, Yiwei Dou, Gordon D. Richardson, Dushyantkumar Vyas

Bibliographic record

VenueJournal of Business Finance &amp Accounting · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaCanadian Academic Accounting AssociationKPMG
KeywordsBusinessSecuritizationBalance sheetAccountingEquity (law)Finance

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.246
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2020
Admission routes2
Has abstractyes

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