Is there a Relation between CEO Remuneration and Banks' Size and performance?
Bibliographic record
Abstract
The goal of the study is to establish what kind of relationship, if any, exists between CEO remuneration and Banks size and performance. The study is extremely relevant, especially in the financial sector after the crisis of 2007-2008. Many critics have argued both as rhetoric as well as an empirical study that high executive pays have a negative impact on the sustainability and success of a firm. Studying the literature reveals plurality in positions. Since this is an extremely complex question, it is understandable that literature exists arguing on both sides of the debate. This paper collected data on Bank Size and Bank Performance for 6 Canadian banks to study their correlation with CEO remuneration. It was hypothesized that there existed a positive relationship between CEO remuneration and Bank Size (measured by Sales, Deposits and Employees) and Bank Performance (measured by ROA, ROE and Profit Margin). The data was put through SPSS for a Pearson coefficient analysis which revealed a strongly positive correlation between CEO remuneration and all three variables of Bank Size. On the other hand, no significant relationship could be established between CEO remuneration and Bank Performance except a weak positive relationship with ROA. The study can be helpful in executive decision making. However, it also calls for further research into external factors that have an impact on the relationships between these variables. Most importantly a comparison study following the same methodology of different regions can give us more useful business intelligence and insight
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How this classification was reachedexpand
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 | 0.003 |
| 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 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".