Bibliographic record
Abstract
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.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".