Impact of Covid-19 Pandemic on the Financial Performance of the Banking Sector of Bangladesh
Bibliographic record
Abstract
Covid-19 pandemic has affected both real and nominal sectors of all countries in the world. The paper has examined the impact of this pandemic on the banking sector of Bangladesh. Using ratio and correlation analysis on 2019 and 2020 data some interesting findings are invented. Pandemic has incurred devastating and homogeneous impacts for all type of banks in Bangladesh. Profitability and efficiency have decreased sharply. The analysis shows that if the provision criteria had not been relaxed, the profit rate would have been decreased further. Negative growth of profitability and interest earning by the most banks are very glaring. Aggregate profit of the banking system has fallen about 4 percent. Further, evidence of investment scope shrinking is also manifested. Hence, sheer income rearrangement is also detected by the banks in pandemic situation. The loss caused by the decrease of interest earning was attempted to offset by the increase of fee based income. However, productivity is remained largely unchanged during the pandemic. Additionally, drastic fall of major balance sheet items in 2020 are also noticed. Banks were desperate to save themselves from the tsunami of losses by searching alternative sources of business too. Additionally, profit fall and international linkage of individual banks have very high association. The paper has very high stake particularly for crisis management of the banking system of developing countries where banks are suffering from the less variety of product and lending activities.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".