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Bank Specific and Macroeconomic Determinants of Non-Performing Loans of Nepalese Commercial Banks A Balanced Panel Data Analysis

Barsha Dhakal*
MBA-BF Scholar, Lumbini Banijya Campus, Nepal

*Corresponding author

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Abstract
The determinants of credit risk need to be established because non-performing loans (NPLs) are a major threat for the stability and performance of commercial banks. This study aims to explore the factors that influence the NPLs of the commercial banks of Nepal with the help of a balanced panel data with 10 commercial banks and 120 bank-year observations for the period 2013/14 to 2024/25. It uses the Panel data regression analysis techniques with the Random Effects Model being chosen by the findings of the Breusch-Pagan Lagrange Multiplier Test and Hausman Specification test. The result revealed that the overall model explains almost 59.5 percent of the variations in NPLs of the commercial banks in Nepal. This further showed that among the variables used in the study, Return on Assets and Capital Adequacy Ratio have negative influence on the NPLs whereas Bank Size has positive influence on NPLs, but Credit Growth does not have any influence on NPLs. Conversely, GDP Growth, Inflation and Unemployment have a positive impact on NPLs while Weighted Average Lending Rate (WALR) has a negative impact on NPLs. Therefore, it is concluded that the credit risk of Nepalese commercial banks is affected by both bank specific and macroeconomic factors and that bank specific factor has the greater effect on the credit risk of commercial banks in Nepal in comparison to macroeconomic factor. This study supplements the knowledge on risk management practices of commercial banks of Nepal and particularly on NPL in Nepal. The study's findings have several managerial and policy implications regarding improvement of the credit risk management strategies of commercial banks and Nepal Rastra Bank respectively. Keywords: Non-performing loans; Credit risk; Commercial banks; Bank-specific determinants; Macroeconomic determinants; Random Effects Model; Panel data.