Bank Value at Risk (VAR) disclosures. A missed leading indicator to the Financial Crisis of 2008?
Bibliographic record
Abstract
The financial crisis of 2008 led to devastating consequences such as bankruptcies and recession in the US economy. Many big banks were at the forefront owing to their risk exposures and open positions. Prior research documents that bank financial statements did not provide adequate lead indicators on the looming crisis in reducing information asymmetry. However, there is no prior research focused on the sufficiency of risk disclosures around this time period. This paper seeks to address this gap using Bank Value at Risk (VAR), a single number publicly disclosed in the annual reports of banks. Bank VAR attempts to quantify the worst possible loss the bank expects to have on its trading portfolios under normal market conditions. Using hand-collected data from the annual reports of the top twelve US banks, this study documents that the change in VAR was steady and positive until the point of the crisis and then decreased in the years thereafter. A repeated-measures analysis of variance model is used to study whether two indicators of VAR (year-to-year change in VAR and log-transformed ratio of VAR to the total trading revenue) differ from pre-crisis to the post-crisis levels. Both VAR indicators reveal an increasing trend pre-crisis and are significantly higher pre-crisis compared to post-crisis. This opens the possibility that the trend of VAR might have information content as a potential leading indicator of the crisis. The finding sheds light on efficacy of risk analysis in bank trading portfolios and could have implications for governance.
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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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".