Empirical Analysis of X-efficiency and Profitability of Commercial Banks in Canada
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
This paper will examine the efficiency and profitability of Canadian banking sector. The idea is to take a two-stage approach to test determinants of profitability over the period of 2010-2016 with respect to bank’s efficiency performance. In the first stage, the model will be focused on estimations using Stochastic Frontier Analysis (SFA) of cost functions to get the efficiency score for Canadian banks. Then in the second stage, estimations using Generalized Method of Moments (GMM) will be modeled to find out the significant variables influencing bank’s profitability, along with consideration of the efficiency score from the first stage and calculated Z score along with internal and external variables. Results indicate that efficiency has positive and significant impact on bank’s profitability, while Z score and other factors demonstrate mixed effects.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 it