Are the Recent Restatements of Financial Institutions 10K’s due to the Perceived Earning Volatility Caused by SFAS 161?
Bibliographic record
Abstract
The goal of this research was to investigate the controversy surrounding the inability of SFAS 133 an amendment of SFAS 161 to portray the economics of hedging. This research examined whether or not BHCs’ design of hedge effectiveness tests was determined by the concern of the additional earnings volatility possibly evolved from economic hedges that do not qualify for hedge accounting. The results implicate that most BHCs after the amendment of SFAS 161 reassessed their risk management approach to one that is more accounting responsive to ensure that most hedges are highly effective to qualify for hedge accounting. The findings suggest that BHCs reciprocate between risk management and earnings volatility when face a trade-off between employ economic hedges which increase earnings volatility and discontinue economic hedges to avoid increases in earnings volatility. The results accede with the results of Park (2004), Singh (2008), Zhang (2008), Hariom (2014), Bratten (2016), Spencer (2018), and Thomas (2018) who found that derivative users had lower levels of earnings volatility after the introduction of SFAS 161.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".