Has ‘Too Big To Fail’ Been Solved? A Longitudinal Analysis of Major U.S. Banks
Bibliographic record
Abstract
In the wake of the global financial crisis that erupted in 2008, there has been extensive commentary and regulatory focus on the ‘Too Big to Fail’ issue. In this paper, we survey the proposed solutions and regulatory initiatives that have been undertaken. We conduct a longitudinal analysis of major U.S. banks in four discrete time periods: pre-crisis (2005–2007), crisis (2008–2010), post-crisis (2011–2013) and normalcy (2014–2016). We find that risk metrics such as leverage and volatility which spiked during the crisis have reverted to pre-crisis levels and there has been improvement in the proportion of equity capital available to cushion against asset value deterioration. However, banks have grown in size and it does not appear as if their business models have been redirected toward more traditional lending activities. We believe that it is premature to conclude that ‘Too Big to Fail” has been solved, but macro-prudential regulation is now much more effective and, consequently, banks are on a considerably sounder footing since the depths of the crisis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".