Loan Growth, Bank Solvency and Firm Value: A Comparative Study of Nigerian and Malaysian Commercial Banks
Bibliographic record
Abstract
The study explore the issues relating to credit growth, non-performing credit and bank solvency in the banking industry, recognizing that existing studies are largely sketchy in emerging and developing markets. Panel data estimation technique is employed in the study based on data extracted from 26 commercial banks in Nigeria and Malaysia over the period 2009 to 2017 making up to 234 observations. The results reveal that the NPLs for all banks is only explained by loan growth and inflation, NPLs for Nigerian banks is only explained by loan growth, leverage, efficiency, size and inflations while NPLs for Malaysian banks is only explained by leverage, efficiency, size, GDP and inflation. The bank solvency for all banks is only explained by NPLs, loan growth and leverage. The solvency for Nigerian banks is explained by NPLs, leverage and GDP while loan growth, size and inflation explained bank solvency for Malaysian banks. Firm value for all banks is explained by solvency, NPLs, leverage, efficiency, size and GDP, the value of firm for Nigerian banks is only explained by solvency, loan growth, leverage, efficiency and size. The firm value for Malaysian banks is only explained by solvency, loan growth, leverage, efficiency, size, GDP and inflation. It is observed that bank solvency play an important role in the firm value of commercial banks in the period of study. Hence, this paper contributes to the understanding of the dynamic role of abnormal loan growth and how it can enhance the volume of non-performing credit and suggest that further study can explore the interaction between abnormal loan growth and non-performing loans.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".