Use of CAMEL Rating Framework: A Comparative Performance Evaluation of Selected Bangladeshi Private Commercial Banks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Banking sector in Bangladesh is one of the fast growing sectors and considered as an integral part of the economy. Hence, monitoring, supervision and continuous performance evaluation of the banking sector is compulsory to ensure the financial stability of the economy since the banking sector is becoming more complex than before. The present study is an attempt to evaluate and compare the performance of the banking sector in Bangladesh. One of the most effective supervisory techniques, CAMELS rating system (basically a quantitative technique) has been used to rank the banks based on their performances. In this study, seventeen conventional private commercial banks have been chosen as samples to meet the purpose of the study. Data for analysis has been collected from the banks’ annual reports for the period (2010-2016). The result from this comparative analysis shows that Eastern Bank has stood at the top position among all the selected banks based on CAMEL rating system. However, the findings from this paper will definitely help the researchers and analysts to understand financial statement analysis in a depth manner and also provide a uniform basis for identifying those institutions requiring special supervisory attention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 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 | 0.001 |
| Open science | 0.001 | 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 it