Equity Capital as a Safety Cushion in the US Banking Sector
Bibliographic record
Abstract
The incidence of US bank failures soared in the financial crisis and economic recession starting in 2008. Financial regulations promulgated by the Federal Reserve and issued through the Basel III Accord raised the minimum equity capital requirements of banks. The intent of the increase in equity capital was to serve as a greater safety cushion to reduce the probability of failure. The purpose of this study is to examine the financial statement variables that distinguish failed (zero equity capital) and nonfailed US banks. The methods employed to investigate our research question are: 1. univariate t-test, and 2. tobit regression analysis with equity capital as the dependent variable. Our results show that the factors explaining equity capital include real estate loans to assets, equity capital to total assets, log of total assets, return on equity, loan loss allowance to total loans, non-performing loans to total assets, total loans to total assets, mortgage-backed securities to total assets, total short-term debt securities to total assets, net gains on sales of loans to total non-interest income, and insured deposits to total deposits. Bank management and financial regulators need to focus on these financial characteristics to ensure adequate equity capital as a safety cushion.
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 | 0.002 | 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 | 0.000 | 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 | 0.000 |
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".