Retail Banking and Bank Performance: Evidence from Nigeria
Bibliographic record
Abstract
This is a correlational research design that intended to examine the nexus between retail banking and financial performance of banks in Nigeria. The Panel Least Squares regression results aided the study in ascertaining the coefficient, standard error, t-statistic and probability. The tabulated ratios were exported to EViews 9.0 to run the panel regression. Data were collected and analyzed based on the annual reports available on the website of 16 banks listed on the Nigerian Stock Exchange as at 31st December 2018 out of the approved 22 commercial banks in Nigeria. The result of findings revealed that Size and Competition significantly impacted on bank performance with a probability of 0.0071 and 0.0178 respectively which is less than 5% degree of significance; and Loans and deposits relationships were all not significantly impacting on bank performance, as the probabilities for all variables were more than the acceptable 5% degree of significance. Based on the outcome of findings the following conclusions were drawn that; Size which is the natural logarithm of Total Assets and Competition which is the natural logarithm of Total Deposit is significantly responsible for bank performance in Nigeria. The study therefore recommended that professional bankers should continually focus on growth and expansion drive that will increase the size of the bank and enable the banks to compete nationally and internationally in order to drive profitability. Banks that want to experience optimum performance should focus more on policies that will attract huge retail deposit to the bank being that the savings component of the total deposit banks in Nigeria comes at a ridiculously cheap rate to the banks and banks loaned out these deposits at high margin.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".