Risk Management Practices and Financial Performance: Analysing Credit and Liquidity Risk Management and Disclosures by Nigerian Banks
Bibliographic record
Abstract
Nigerian banks encounter persistent difficulties in efficiently managing and disclosing credit and liquidity risks, considerably affecting their financial performance and shareholders’ confidence. This study, therefore, examined the effect of risk-management practices and disclosures on the financial performance of Nigerian commercial banks. The population of the study comprised 13 Nigerian commercial banks, of which 12 were purposively chosen, subject to data availability. The data explored in this study originate from World Development Indicators and the annual reports and accounts of the selected Nigerian commercial banks from 2012 to 2023. The data analysis technique used was panel regression analysis, which was further extended to the generalized method of moments in a bid to account for potential endogeneity. The study made use of EViews 12 software to analyse the data. The results reveal that liquidity risk disclosure and firm size had significant and positive effects on financial performance, while credit risk disclosure, credit risk, firm age, and leverage had significant and negative effects. This study concludes that credit risks significantly undermine commercial banks’ financial performance, as an upsurge in non-performing loans results in reduced financial performance. Conversely, effective liquidity risk disclosure characterized by transparent reporting on liquidity position was found to enhance financial performance. This study, therefore, recommends, among others, that banks should strengthen their credit risk assessment framework and enhance transparent risk reporting to improve performance and financial stability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".