Does Fair Value Accounting Contribute to Systemic Risk in the Banking Industry?
Bibliographic record
Abstract
ABSTRACT I investigate whether fair value accounting can contribute to the banking industry's systemic risk. I focus on the adoption of Statement of Financial Accounting Standard No. 115 (SFAS No. 115), which required available‐for‐sale (AFS) securities to be recognized at fair value with unrealized gains and losses included in equity through accumulated other comprehensive income. SFAS No. 115 increased banks' regulatory risk because, at the time, calculation of regulatory capital closely conformed with GAAP equity. I find that systemic risk increased following the adoption of SFAS No. 115. Furthermore, following a subsequent regulatory amendment—which excluded unrealized gains and losses on AFS securities from regulatory capital but did not change their GAAP treatment—systemic risk decreased. Taken together, the evidence suggests that fair value accounting has the potential to increase systemic risk through the explicit inclusion of volatile fair value estimates in regulatory bank capital adequacy assessments. I do not, however, find evidence of fair value accounting impacting systemic risk in its information role; that is, by providing information to a bank's external stakeholders about its financial position and performance. I also show that higher fair value volatility of investment securities, lower bank capital, and larger AFS security holdings increase banks' marginal contribution to systemic risk. My findings should interest regulators and policymakers, as recent regulatory changes in light of Basel III recommendations require unrealized gains and losses on AFS securities to be included in regulatory capital for advanced approaches banks.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".