<scp>An Examination of Other‐than‐temporary Impairments</scp>: <scp>Evidence from FSP FAS</scp> 115‐2 and <scp>FAS</scp> 124‐2
Bibliographic record
Abstract
This study investigates amendments to the other‐than‐temporary impairment (OTTI) measurement and recognition guidance for debt securities in the Financial Accounting Standards Board's Staff Position FAS 115‐2 and FAS 124‐2 (‘the FSP’). The FSP permits the OTTI charge for debt securities to be split into the credit loss amount recognized in net income (NI) and the amount related to all other factors (noncredit loss) recognized in other comprehensive income (OCI) under certain instances. Thus, the FSP provides banks with additional discretion in recognizing the amount of unrealized losses in NI. Using quarterly accounting data on US bank holding companies from the first quarter of 2010 to the fourth quarter of 2016, I examine whether banks’ decisions regarding OTTI bifurcation are associated with financial reporting and regulatory capital incentives. First, I predict and find that proxies for regulatory capital management, income smoothing, and big bath behaviour impact the percentage of OTTI recognized in OCI. Further evidence suggests that banks with histories of income smoothing through loan loss provision and realized security gains and losses assign a greater share of OTTI to OCI. Then, I predict and find that banks manage OTTI recognized in NI downward to meet quarterly financial reporting benchmarks. Finally, I show that banks with a higher discretionary proportion of OTTI recognized in OCI increase their lending over the subsequent quarter.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".