Product-mix and bank performance: new U.S. and Canadian evidence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it