Bibliographic record
Abstract
This paper aims to examine the shareholder value efficiency and build a Shareholder Value Index for Saudi banks between 2010 and 2014. Shareholder value efficiency is measured using value based performance metrics and binary logistic regression model was adopted to develop the Shareholder Value Index for Saudi banks. The Shareholder Value Index developed provides the probability of a bank’s competence to create/erode shareholders' wealth. The study finds that Economic Value Added (EVA) is the value-based performance metric that comes closer than any other to capture the true Economic Profit and the market performance (Market Value Added) of banks. Positive EVA of most of the commercial banks denote that they are more Shareholder value efficient than Islamic banks. High Market Value Added (MVA) represents a highly positive outlook of the investors on the Saudi banks' performance. Value creation is significantly linked to high Net Operating Profit After Tax and a low cost of capital. The most significant observation is that not all banks with highest capital employed are the highest value creators. The Shareholder Value Index developed indicate that majority of Saudi banks demonstrate a higher probability of shareholder value creation. Few Islamic banks showed a less probability of wealth creation for the period of study and are predicted to improve the shareholder value creation ability through their aggressive strategy in the future.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".