Investigating the Performance of Islamic Banks in Bangladesh
Bibliographic record
Abstract
Around the world Islamic banking system is getting popularity gradually due to its multidimensional benefits. Consequently, many tradition banks have been converted (such as FSIBL and EIBBL) into Islamic Sharia’h based banks for the superiority of Islamic banking system. Thus, it is the curiosity of the investors, depositors, researchers and policy makers to know the performance of Islamic banks operating in Bangladesh. So, taking secondary data from the annual reports of the sample banks, the study has evaluated the performance of six Islamic banks listed at both Dhaka Stock Exchange (DSE) & Chittagong Stock Exchange (CSE) in terms of deposit; investment; foreign remittance collection; earnings per share (EPS); dividend declaration; dividend payout ratio; price earnings ratio (P/E) and net asset value (NAV). The study found that six Islamic banks have performed very well. Especially; Islami Bank Bangladesh Ltd. has shown outstanding performance in terms of every indicator. It is expected that the study will not only help the investors and depositors to make their decisions in more efficient way but also; it will motivate non-Islamic banks to convert their business mode according to Islamic Sharia’h.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".