The impact of natural disasters on the performance and solvency of US banks
Bibliographic record
Abstract
Purpose This paper explores the effect of natural disasters on the profitability and solvency of US banks. Design/methodology/approach Employing a sample of 187 large-scale natural disasters that occurred in the United States between 2000 and 2014 and a sample of 2,891 banks, we examine whether and how disaster-related damages affect various measures of bank profitability and bank solvency. We differentiate between different types of banks (with local, regional and national operations) based on a breakdown of their state-level deposits and explore the reaction of these banks to damages weighted by the GDP of the states they operate in. Findings We find that natural disasters have a pronounced effect on the net-income-to-assets and the net-income-to-equity ratio of banks, as well as the banks' impaired loans and return on average assets. We also observe significant effects on the equity ratio and the tier-1 capital ratio (two solvency measures). Interestingly, the latter are positive for regional banks which appear to benefit from increased customer deposits related to safekeeping, government payments for post-disaster recovery, insurance payouts and decreased withdrawals, while they are significantly negative for banks that operate locally or nationally. Originality/value We contribute to the literature by offering various new insights regarding the effects natural disasters have on financial institutions. With climate change-driven natural disasters widely expected to increase both in terms of frequency and severity, their economic fallout is likely to impose an increasing burden on financial institutions. Large, nationally operating banks tend to be well diversified both geographically and in terms of their product offerings. Small, locally operating banks, however, are increasingly at risk – particularly if they operate in disaster-prone areas. Current banking regulations generally do not factor natural disaster risks into their capital requirements. To avoid the next big financial crisis, regulators may want to adjust their reserve requirements by taking this growing risk exposure into consideration.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".