A Study on the Impact of Capitalization on the Profitability of Banks in Emerging Markets: A Case of Pakistan
Bibliographic record
Abstract
A strong capitalized position of financial institutions is essential to ensure their solvency. Because of their unique nature, banks must always keep an optimum level of capital to ensure smooth banking earnings. Consequently, it is mandatory for all types of banks operating in Pakistan to keep a minimum amount of required capital along with capital adequacy to remain solvent and profitable. Therefore, using three measures of capitalization, i.e., the Capital Ratio (CR), Capital Adequacy Ratio (CAR), and Minimum Capital Requirement (MCR), and four measures of profitability, i.e., Return on Avg. Assets (ROAA), Return on Avg. Equity (ROAE), Net Interest Margin (NIMAR), and Profit Margin (NMAR), this study contributes to the existing literature on the relationship between the capitalization and profitability of 29 Pakistani banks over the period of 2007–2018. The results, based on the Generalized Method of Moments (GMM) system estimator technique, reported an inverted U-shaped relationship between the two capitalization measures, i.e., CR and CAR, and the four profitability measures, i.e., ROAA, ROAE, NIMAR, and NMAR. This indicates that profitability increases with an increase in capitalization up to a certain level, while beyond that level, a further increase in capitalization decreases profitability. The results also indicate that banks who maintain their MCR have higher profitability than those who do not.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 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".