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Record W1513224068 · doi:10.1108/14757700710750856

Accounting for transferring financial assets

2007· article· en· W1513224068 on OpenAlexaff
Flora Niu

Bibliographic record

VenueReview of Accounting and Finance · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsSecuritizationValuation (finance)Financial statementEarningsStock (firearms)BusinessFinanceEconomicsAccountingStock marketFinancial systemAudit

Abstract

fetched live from OpenAlex

Purpose This study examines the stock market valuation of securitization gains reported by using the financial‐components approach under FAS125 (Accounting for Transfers and Servicing of Financial Assets and Extinguishment of Liabilities). Design/methodology/approach Based on a sample of US securitizing firms for the period from 1993 to 2000, I conduct two analyses to examine the extent to which the securitization gains reported under FAS125 are reflected in the stock market valuation. Findings The paper finds that the reported gains are positively associated with the stock returns, suggesting that the reported gains are perceived to be value relevant, and that investors appear to use gains in the same manner as they use other earnings information. In addition, it is found that the association between returns and securitization earnings is stronger in the post‐FAS125 period than in the pre‐FAS125 period, suggesting that the financial‐components approach improves the capacity of reported financial statement information to explain stock returns when compared with pre‐FAS125 regulations. Research limitations/implications The results of this study should be helpful to standard setters who continue to apply the financial‐components approach for securitizations and for other transactions involving financial instruments.

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)0.003
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.908
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.008
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 head0.010
GPT teacher head0.239
Teacher spread0.229 · 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 designNot applicable
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

Citations3
Published2007
Admission routes1
Has abstractyes

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