Credit Risk Management and the Financial Performance of Deposit Money Banks: Some New Evidence
Bibliographic record
Abstract
This study examined credit risk management and return on equity of Nigerian deposit money banks (DMBs) twelve (12) years (2010–2021) post-adoption of the common accounting year-end as mandated by the Central Bank of Nigeria (CBN) in 2009. Our data set comprises independent variables of capital adequacy ratio (CAR), liquidity ratio (LQR), loan-to-deposit ratio (LDR), risk asset ratio (RAR), non-performing loans ratio (NPLR), loan loss provision ratio (LLP), and size (SZ). Our dependent variable is the return on equity (ROE). Using a panel data regression analysis, we found that CAR, RAR, NPLR, and SZ are the significant determinants of ROE. We also found that Nigerian DMBs now significantly rely on offshore borrowings in Eurobonds to create risk assets to overcome CBN’s constriction on using local depositors’ funds to create risk assets. Furthermore, we found that shareholders of DMBs with international banking licenses in Nigeria within the study period were not significantly more compensated for their risk exposure than investors in risk-free assets (treasury bills). Therefore, the CBN should continue strengthening its regulatory functions with regular reviews that would compel improvements of the DMBs’ credit risk management systems to mitigate the likely failure of the credit life cycle of granted loans. Additionally, a review of its current regulatory cash reserve ratio of 37.5% is imperative to reduce DMBs’ dependence on offshore funding and its associated foreign exchange risk.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".