Influence of Macro-Economic Factors on Financial Performance of Commercial Banks in Tanzania
Bibliographic record
Abstract
This study was designed to determine the influence of macro-economic factors on financial performance of commercial banks in Tanzania. The study was geared towards achieving the following objectives (i) to assess the influence of interest rate on the financial performance of commercial banks in Tanzania (ii) to determine the influence of inflation rate on the financial performance of commercial banks in Tanzania and (iii) to determine the influence of exchange rate on the financial performance of commercial banks in Tanzania. Data were collected using secondary source. The study applied descriptive and explanatory research design to describe trend of the exchange rate, interest rates and inflation rate for the period of 10 years from 2010 to 2019 in which the relationship between macroeconomic variables mentioned and the financial performance of commercial banks were explained. The data collected for this study was keyed into excel sheet and then the descriptive and correlation analysis was conducted. The study indicates that the correlation analysis conducted between interest rate and return on assets reveal a strong negative relationship of 74.99%. This means that increase in return on assets results in a decrease in interest rate by 74.99%. Further more it is indicated that the correlation analysis conducted between inflation rate and return on assets reveal a positive relationship of 59.22%. Also, the study showed that the correlation analysis conducted between exchange rate and return on assets reveal a negative relationship of 65.52%. The study concluded that interest rate, inflation rate and exchange rate influence financial performance of commercial banks. The study recommended that the government to continue maintaining the lending interest rate to curb a drop in financial performance of commercial banks, to continue maintaining the policies that protect commercial banks in case of inflation rate rising and lastly, the government to continue maintaining the policies that protect commercial banks in case of exchange rate fluctuations.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".