The Timeliness of Accounting Write-Downs by U.S. Financial Institutions During the Financial Crisis of 2007-2008
Bibliographic record
Abstract
This study examines the timeliness of write-downs taken by U.S. financial institutions during the financial crisis of 2007-2008. The timeliness of write-downs is measured by benchmarking the quarterly accounting write-down schedule with the devaluation schedule implied by credit indices such as the ABX. The results show that accounting write-downs are less timely than the devaluations implied by credit indices. In a cross-sectional analysis of the determinants of the timeliness of write-downs, I document that higher corporate governance quality is positively related to timelier write-downs. Furthermore, I observe that regulatory investigations and litigation pressure are positively related to the timeliness of write-downs, whereas the write-downs by firms with more complex exposures, higher financial leverage, and tighter regulatory constraints are less timely. In addition, I control for numerous exposure-specific characteristics and document that less risky exposures, and exposures that were affected later during the financial crisis, were written down later. Regarding the consequences of timeliness, this study finds that the exposure to risky assets is reflected faster in stock returns for firms with timelier write-downs.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".