Does Board Collective Suitability Affect Performance and Risk? Evidence from European Banks
Bibliographic record
Abstract
This paper analyzes the features of the boards of large listed European banks and their degree of “collective suitability” as formalized by the Capital Requirements Directives (CRD4) and evaluates whether closer proximity to the collective suitability regulatory paradigm affects banks’ performance, risk and risk-adjusted performance. We leverage Self-Organizing Maps (SOMs) to analyze board features and suitability (i.e. competence, diversity, independence and time commitment) jointly as a multifaceted, non-linear combination of all board variables, rather than evaluating the single variables individually as in the mainstream literature. Using a hand-collected dataset based on numerous features of boards of directors, we find that European banks’ boards can be classified in four different board archetypes characterized by different degrees of collective suitability. Our findings also suggest positive relationships between the degree of collective suitability and performance, risk-adjusted performance, and risk, confirming that the regulatory provisions on governance are going in the right direction, enhancing effective and prudent management.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".