Does Diversification Improve Bank Efficiency? ∗
Bibliographic record
Abstract
Regulatory constraints and agency problems may prevent banks from realizing an optimal level of diversification in portfolios of business activity and in loan portfolios. Hence banks can be over- or-under diversified. In this paper, we investigate how Canadian bank efficiency is affected by regional, industrial portfolio concentration as well as concentratio in banks ’ business lines and financing sources. Acharya et. al. find that (industrial and broad sectorial) diversification in loans portfolio reduces bank returns while concurrently producing a riskier bank portfolio for a sample of Italian banks. For Canadian banks, similar tests indicate that while industry diversification reduces returns, it also reduces risk. The framework employed Acharya et. al. thus cannot evaluate whether Canadian banks should focus or diversify. This paper measures the efficiency of financial institutions using a portfolio-allocation approach, which derives an efficient (risk-return) frontier for the ‘big-5 ’ banks over 4 risky activities using quarterly financial statements between 1997Q1 and 2003Q2. The distance of an observed bank portfolio’s risk-return combination from the tangency portfolio on the efficient frontier is a measure of bank inefficiency. We analyze the determinants of bank inefficiency.
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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 | 0.011 |
| 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 | 0.000 |
| 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".