Product-mix and bank performance: new U.S. and Canadian evidence
Bibliographic record
Abstract
Purpose – The purpose of this paper is to analyse the link between product-mix and bank performance with a comprehensive look at the contribution of each component of banking activities. Design/methodology/approach – The generalized method of moments estimation approach the authors apply to the US and Canadian large data sets deals with the endogeneity issues related to banks’ decision to diversify in fee-based activities, and the authors also control the non-linearities (asymmetries) in the innovation with a complementary EGARCH procedure. Findings – The results suggests that the increasing involvement of banks in fee generating activities has a greater positive impact on US bank performance. On the one hand, US banks are more involved in fees related to traditional lending activities and securitization, which contributes to their higher mean return. On the other hand, Canadian banks focus more on investment banking activities, which makes their financial results more procyclical and volatile. Greater profitability notwithstanding, the authors also found that US bank non-interest income activities incorporate more credit risk, a type of risk obviously less diversifiable when credit shocks occur. Originality/value – The approach shows that the endogeneity problems related to the banks’ decision to diversify in non-traditional activities may be important. The multivariate GARCH approach the authors introduced strongly suggests that diversification gains fluctuate over the business cycle, and that the decision to diversify must be understood in a dynamic setting rather than in a static one.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".