The Impact of Globalisation Towards Bank Performance in Malaysia
Bibliographic record
Abstract
The purpose of this study is to shed some crucial light on the relationship between globalisation and performance of the banking system in Malaysia. This study uses a range of bank-characteristic determinants (internal factors), macroeconomic determinants (external factors) and three different dimensions of globalisation including economic globalisation, social globalisation and political globalisation to explain local commercial bank performance in Malaysia. This study uses regression analysis based on the secondary data for local commercial banks in Malaysia. The period for this secondary data is 10 years which is from the year 2008 till 2017. This study indicates that there is strong evidence stating both economic and politic globalisation have negatively significant effects on the bank performance in Malaysia. Meanwhile, social globalisation shows an insignificant result on this. As for bank characteristics variables, credit risk shows a negatively significant result towards bank performance in Malaysia while bank size shows a positive and significant result towards bank performance in Malaysia. Sole macroeconomic variable which is GDP does not show any significant result towards the bank performance in Malaysia. Therefore, central bank of Malaysia should give some incentive training for local bankers on how to adopt new supervision and risk management. This will give the local bankers some new knowledge to handle better risk management and directly boost the bank performance. Besides that, banks should develop their credit risk management to overcome any default loans and for better financial performances. Banks in Malaysia also need to expand their businesses as larger banks give a larger facility which directly boots the bank performance. It is also recommended for Malaysian banks to improve their forecasting of macroeconomic fluctuations in future to achieve greater efficiency levels.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".