The Determinants of Profitability of Non-Bank Financial Institutions in Bangladesh
Bibliographic record
Abstract
The non-bank financial institutions (NBFI) industry is considered to be an important source of financing in any economy. Stability of earnings is one of the pre-conditions for survival and growth of any industry in the long run. Keeping the importance of profitability in mind, this paper tries to find out the major financial factors affecting the profitability of the NBFI industry in Bangladesh and evaluate the aspects of the findings. The data was collected for 12 different NBFIs for a period of five years (2013-2017). Return on equity was defined as the dependent variable while firm size, capital adequacy ratio, loan ratio, non-performing loan ratio, deposit ratio, net interest margin, non-interest income margin and cost to income ratio were identified as explanatory or independent variables. Multiple regression analysis was conducted on the data to test the research hypotheses. The findings of the study show that capital adequacy ratio, deposit ratio, non-performing loan ratio and net interest margin were statistically significant at 5% level. Firm size, loan ratio, net interest margin and non-interest income margin show positive relationship with profitability whereas capital adequacy ratio, deposit ratio, non-performing loan ratio and cost to income ratio show negative relationship with profitability. Non-performing loan ratio and net interest margin were found to have a considerable impact on profitability of NBFIs. This is further supported by the fact that non-performing loans do not generate any income and net interest income is considered the main source of income for a financial institution. The study recommends that the NBFIs in Bangladesh give due attention to these factors to improve their financial performance.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".