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Abstract
Non-performing loans (NPLs) rate is one of the main risks in commercial banks and is also a critical measure of the bank’s financial performance and stability. Banks meet the growth rate of NPLs when the debtors are not able to meet their financial obligations in terms of repayment of loans. Regional diversification can impact NPLs rate as well as macroeconomic and bank-specific factors. The purpose of this study is to detect homogeneous credit risk groups by geographical locations. Diversification across regions can help banks and financial institutions to determine appropriate market areas and identify effective diversified investment strategies by reducing the overall risk of the credit portfolios.
Highlights
There is a growing recognition that the quantity or percentage of non-performing loans (NPLs) is related to bank failures and the financial status of a country
NPLs rate may vary by region even under the same economic conditions
In order to choose the right number of cluster and to evaluate clustering results, Silhouette (S), Davies -Bouldin (DB), Calinski-Harabasz (CH), Dunn (D), Krzanowski-Lai (KL) and Hartigan (Han) validity indices and visual cluster validity (VCV) are used
Summary
There is a growing recognition that the quantity or percentage of non-performing loans (NPLs) is related to bank failures and the financial status of a country. There are some evidences that financial and banking crisis in East Asia and Sub-Saharan African countries were preceded by increasing non-performing loans. In this view of this reality, the non-performing loan ratio is, a critical measure to evaluate a bank’s performance, the economic activity, and the national financial stability and soundness. The literature generally classifies these factors into two parts, namely: macroeconomic and bank-specific factors Beside these factors, NPLs rate may vary by region even under the same economic conditions. NPLs rate may vary by region even under the same economic conditions From this point of view, the purpose of this study is to find homogeneous credit risk groups by geographical locations. In order to choose the right number of cluster and to evaluate clustering results, Silhouette (S), Davies -Bouldin (DB), Calinski-Harabasz (CH), Dunn (D), Krzanowski-Lai (KL) and Hartigan (Han) validity indices and visual cluster validity (VCV) are used
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