Lurking in the shadows: the opportunity cost of tackling asset quality in European banking
Bibliographic record
Abstract
This paper reveals the unforeseen or opportunity cost of tackling asset quality in European banking using shadow prices from efficiency analysis. The study seeks to understand the impact on loan book growth when reducing non-performing loans. Furthermore, the investigation assesses the opportunity cost implications of supervising a cohort of banks at the supranational level. The study estimates shadow prices using Stochastic Nonparametric Envelopment of Data (StoNED). StoNED is a new unifying framework which fully integrates axiomatic (DEA) and stochastic (SFA) productivity analysis techniques. The analysis introduces a novel statistical test based on group differences in the shadow price estimated using fronter efficiency techniques. The test exploits a trigonometric relationship in the isoquant construct, which allows statistical differences in shadow prices to be tested using input and output variables in ratio form. Importantly, the test is independent of the estimation of the frontier and thus free from bias introduced by group differences. The results reveal that the cost of tackling asset quality is economically meaningful and raising. In 2010, a typical bank experienced a 24 basis point yield loss per €1M reduction in non-performing loans, with this value increasing three-fold to 69 basis points by 2016. Furthermore, banks supervised at the supranational level experience a significantly higher shadow price than their nationally supervised counterparts. Our novel test shows that these cohort differences are statistically significant for all years in our 2010-2016 sample. For example, in 2016, the annual loss on yield from high-quality loans is 50 basis points higher for bank supervised at the supranational level. Our findings suggest that tackling asset quality may have a significant impact of loan book yield and thus the growth of the high-quality loan books at European. This impact will be especially challenging for bank supervised at the supranational level, and the current two-tier supervisory system may be curtailing sustainable asset growth.<br/>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.059 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 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 teacher head, 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".