Ireland’s retail banking crisis: lessons to learn and policy implications
Bibliographic record
Abstract
Lessons have been learned since the Irish banking crisis, and important regulatory and\nsupervisory actions have been taken both domestically and internationally. While, there\nexists an extensive body of research investigating the Irish banking crisis, a number of\nimportant questions remain unanswered in relation to whether the latent distress in the\nIrish retail banking system could have been recognised contemporaneously. To address\nthese gaps, this thesis builds upon the existing literature in two ways. First, the main\nIrish banks are compared to a European sample of peers across a unique database of\nfinancial indicators using econometric analyses to see if the severe financial distress in\nwhich they found themselves could have been identified earlier. Secondly, using a case\nstudy approach, this thesis presents an original and detailed comparative analysis of the\nCanadian and Spanish retail banking systems to investigate whether any regulatory and\nsupervisory lessons can be identified. These countries’ commercial retail banks provide\na useful benchmark given their relative resilience during the Global Financial Crisis.\nThe main findings from this research can be summarised as follows: (1) Statistical\nevidence is presented which shows structural differences in the lead up to the crisis\nbetween those banks that had to be bailed out and those that did not. In particular,\nfunding structure was the most robust predictor of performance – banks with more\ndepository funding experienced a lower probability of being bailed out. (2) In addition,\nrobust funding models and vigorous liquidity management were identified as important\ndeterminants of banking performance. The Spanish case study found that Spanish\nbanks were far more internationally diversified than their Irish peers – their balance\nsheets thus provided greater access to capital to cushion the problems they faced when\nthe Spanish real estate sector collapsed. The Spanish banks’ unique use of\ncountercyclical provisioning was also found to be a key differentiating factor. The\nCanadian case study showed that Canadian banks had higher capital levels, relied more\nheavily on equity and used more deposit-based funding structures compared to their\nIrish peers.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".