Were Information Intermediaries Sensitive to the Financial Statement‐Based Leading Indicators of Bank Distress Prior to the Financial Crisis?
Bibliographic record
Abstract
Abstract In this paper, we address two questions that emerged in the aftermath of the 2008 financial/banking crisis. First, did the financial statements of bank holding companies provide an early warning of their impending distress? Second, were the actions of four key financial intermediaries (short sellers, equity analysts, Standard and Poor's credit ratings, and auditors) sensitive to the information in the banks’ financial statements about their increased risk and potential distress? We find a significant cross‐sectional association between banks’ 2006 Q4 financial information and bank failures over 2008–2010, suggesting that the financial statements reflected at least some of the increased risk of bank distress in advance. The mean abnormal short interest in our sample of banks increased from 0.66 percent in March 2005 to 2.4 percent in March 2007 and the association between short interest and leading financial statement indicators also increased. In contrast, we observe neither a meaningful change in analysts’ recommendations, Standard and Poor's credit ratings, and audit fees nor an increased sensitivity of these actions to financial indicators of bank distress over this time period. Our results suggest that actions of short sellers likely provided an early warning of the banks’ upcoming distress prior to the 2008 financial crisis.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".