Factors that affect the financial performance of Bank Islam Malaysia Berhad in Malaysia
Bibliographic record
Abstract
This paper aims to find the factors that affect the financial performance of Bank Islam Malaysia Berhad (ROE). The variables are bank size (TA), management quality (ER) and capital (CAR). The sample of this study is comprises of 40 observations on a quarterly basis for 10 years periods from Quarter 1 2007 until Quarter 4 2016. The methodology used in this study is Multiple Linear Regression (MLR) with time series data. All the data is collected from the data stream. There are three variables that can affect the financial performance of Bank Islam Malaysia Berhad. The variables are bank size (TA), management quality (ER) and capital (CAR). Interactive software package E-views would be used for testing and analysing the data collected. The result from this study would provide us the main factors that can affect the financial performance of Bank Islam Malaysia Berhad. It could form an important for Bank Islam Malaysia Berhad to improve their performance
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".