Determinants of Non Performing Loans: Evidence from Sri Lanka
Bibliographic record
Abstract
In the recent past, the global financial crisis and the subsequent recession in many developed countries have increased households’ and firms’ defaults, causing significant losses to the banks. Case of Sri Lanka is no difference. The changes in the economic conditions are believed to have a critical role to play in determining the level of nonperforming loans. Regulators all over the world have started to pay more attention to the credit quality of the Banks and strengthened the regulatory frameworks. This paper attempts to study the macroeconomic determinants of banks’ loan quality in Sri Lanka by analyzing secondary data over the period 1998–2014. The methodology to be adopted for the study was arrived at upon careful review of the literature and following the empirical studies conducted on the determinants of the nonperforming loans. The finding of the analysis is that, out of the six determinants, GDP growth rate and the Export Growth are significant in determining the level of the NPLs in the Sri Lankan banking sector. The relationship of the GDP with the NPL is found to be positive which is not consistent with the majority of the empirical findings Keywords: Nonperforming Loans, Macroeconomic Determinants, In the recent past, the global financial crisis and the subsequent recession in many developed countries have increased households’ and firms’ defaults, causing significant losses to the banks. Case of Sri Lanka is no difference. The changes in the economic conditions are believed to have a critical role to play in determining the level of nonperforming loans. Regulators all over the world have started to pay more attention to the credit quality of the Banks and strengthened the regulatory frameworks. This paper attempts to study the macroeconomic determinants of banks’ loan quality in Sri Lanka by analyzing secondary data over the period 1998–2014. The methodology to be adopted for the study was arrived at upon careful review of the literature and following the empirical studies conducted on the determinants of the nonperforming loans. The finding of the analysis is that, out of the six determinants, GDP growth rate and the Export Growth are significant in determining the level of the NPLs in the Sri Lankan banking sector. The relationship of the GDP with the NPL is found to be positive which is not consistent with the majority of the empirical findings Keywords: Nonperforming Loans, Macroeconomic Determinants, In the recent past, the global financial crisis and the subsequent recession in many developed countries have increased households’ and firms’ defaults, causing significant losses to the banks. Case of Sri Lanka is no difference. The changes in the economic conditions are believed to have a critical role to play in determining the level of nonperforming loans. Regulators all over the world have started to pay more attention to the credit quality of the Banks and strengthened the regulatory frameworks. This paper attempts to study the macroeconomic determinants of banks’ loan quality in Sri Lanka by analyzing secondary data over the period 1998–2014. The methodology to be adopted for the study was arrived at upon careful review of the literature and following the empirical studies conducted on the determinants of the nonperforming loans. The finding of the analysis is that, out of the six determinants, GDP growth rate and the Export Growth are significant in determining the level of the NPLs in the Sri Lankan banking sector. The relationship of the GDP with the NPL is found to be positive which is not consistent with the majority of the empirical findings Keywords: Nonperforming Loans, Macroeconomic Determinants, In the recent past, the global financial crisis and the subsequent recession in many developed countries have increased households’ and firms’ defaults, causing significant losses to the banks. Case of Sri Lanka is no difference. The changes in the economic conditions are believed to have a critical role to play in determining the level of nonperforming loans. Regulators all over the world have started to pay more attention to the credit quality of the Banks and strengthened the regulatory frameworks. This paper attempts to study the macroeconomic determinants of banks’ loan quality in Sri Lanka by analyzing secondary data over the period 1998–2014. The methodology to be adopted for the study was arrived at upon careful review of the literature and following the empirical studies conducted on the determinants of the nonperforming loans. The finding of the analysis is that, out of the six determinants, GDP growth rate and the Export Growth are significant in determining the level of the NPLs in the Sri Lankan banking sector. The relationship of the GDP with the NPL is found to be positive which is not consistent with the majority of the empirical findings Keywords: Nonperforming Loans, Macroeconomic Determinants, In the recent past, the global financial crisis and the subsequent recession in many developed countries have increased households’ and firms’ defaults, causing significant losses to the banks. Case of Sri Lanka is no difference. The changes in the economic conditions are believed to have a critical role to play in determining the level of nonperforming loans. Regulators all over the world have started to pay more attention to the credit quality of the Banks and strengthened the regulatory frameworks. This paper attempts to study the macroeconomic determinants of banks’ loan quality in Sri Lanka by analyzing secondary data over the period 1998–2014. The methodology to be adopted for the study was arrived at upon careful review of the literature and following the empirical studies conducted on the determinants of the nonperforming loans. The finding of the analysis is that, out of the six determinants, GDP growth rate and the Export Growth are significant in determining the level of the NPLs in the Sri Lankan banking sector. The relationship of the GDP with the NPL is found to be positive which is not consistent with the majority of the empirical findings Keywords: Nonperforming Loans, Macroeconomic Determinants, In the recent past, the global financial crisis and the subsequent recession in many developed countries have increased households’ and firms’ defaults, causing significant losses to the banks. Case of Sri Lanka is no difference. The changes in the economic conditions are believed to have a critical role to play in determining the level of nonperforming loans. Regulators all over the world have started to pay more attention to the credit quality of the Banks and strengthened the regulatory frameworks. This paper attempts to study the macroeconomic determinants of banks’ loan quality in Sri Lanka by analyzing secondary data over the period 1998–2014. The methodology to be adopted for the study was arrived at upon careful review of the literature and following the empirical studies conducted on the determinants of the nonperforming loans. The finding of the analysis is that, out of the six determinants, GDP growth rate and the Export Growth are significant in determining the level of the NPLs in the Sri Lankan banking sector. The relationship of the GDP with the NPL is found to be positive which is not consistent with the majority of the empirical findings Keywords: Nonperforming Loans, Macroeconomic Determinants, In the recent past, the global financial crisis and the subsequent recession in many developed countries have increased households’ and firms’ defaults, causing significant losses to the banks. Case of Sri Lanka is no difference. The changes in the economic conditions are believed to have a critical role to play in determining the level of nonperforming loans. Regulators all over the world have started to pay more attention to the credit quality of the Banks and strengthened the regulatory frameworks. This paper attempts to study the macroeconomic determinants of banks’ loan quality in Sri Lanka by analyzing secondary data over the period 1998–2014. The methodology to be adopted for the study was arrived at upon careful review of the literature and following the empirical studies conducted on the determinants of the nonperforming loans. The finding of the analysis is that, out of the six determinants, GDP growth rate and the Export Growth are significant in determining the level of the NPLs in the Sri Lankan banking sector. The relationship of the GDP with the NPL is found to be positive which is not consistent with the majority of the empirical findings
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".