This paper investigates the impact of economic indicators in their relationship to non-performing loans and the way the indicators change trough times using a cross-sectional analysis from the sample of 70, 69 and 63 world countries.
Using the robust regression approach, economic indicators were analysed to find their impact on non-performing loans for the period from 2015 to 2017. The results show that the economic indicators negatively relate to non-performing loans are population growth rate (PG), current balance account (CBA), claims on private sector (CPS) and domestic credit to private sector (DCPS).
Economic indicators have a positive impact on non-performing loans are gross domestic product growth (GDPG) and inflation. The findings also show that economic indicators affecting non-performing loans are changing. During the periods analysed, there is an increase in economic indicators affecting non-performing loans. They were only domestic credit to private sector, current balance account and claims on private sector in 2015. In 2016, another factor occurred in addition of those of 2015, inflation. In 2017, additional factors again occurred, population growth and gross domestic product growth.
Banks should take recovery measures to reduce non-performing loans. A better assessment of the repayment capacity of the future customers coupled with a permanent follow-up of the customer during the whole credit cycle should be enhanced. Further investigations are needed to understand the interactions, and the relationships between non-performing loans, and the different types of borrowers, namely; individuals, small and medium enterprises, and corporate borrowers for a better customer selection because an increase in domestic credit to private sector reduces non-performing loans. Banks should develop a credit risk assessment model combining internal and customer factors.
Table of Contents
1. Introduction
2. Literature Review
3. Data and Methodology
4. Empirical results
Objectives and Research Themes
This paper investigates the impact of various economic indicators on non-performing loans (NPLs) and analyzes how these determinants evolve over time, specifically focusing on the period from 2015 to 2017. Using a cross-sectional analysis across 70, 69, and 63 countries respectively, the study seeks to identify key micro and macroeconomic drivers of NPLs and assess their dynamic nature in influencing banking sector stability.
- Impact of macroeconomic indicators on non-performing loans
- Temporal stability and variability of NPL determinants
- Robust regression analysis of banking data
- Comparative cross-sectional country analysis (2015-2017)
- Policy recommendations for credit risk assessment
Excerpt from the Book
1.2. Problem Statement
As it is mentioned previously, in some countries, banking sectors present a high rate of non-performing loans. The consequences of non-performing loans are various. Literature shows that non-performing loans affect not only banks but also borrowers, financial stability and a country economic growth. Muhammad et al., (2016) concluded that non-performing loans hurt economic growth. There is a decline in economic growth when non-performing lows grow and capital requirements will increase as a result of the growth of non-performing loans as erosion of capital occurs due to funds being trapped in such entities, making it impossible for the banks to fund new, economically viable ventures (Akinola & Mabutho, 2016). Non-performing loans contract credit supply, distort allocation of credit; worsen market confidence and slow economic growth (Maria, Michel & Alexander, 2016). If the trend of non-performing loans is not decreased, many countries will not be able to develop their economy. Many researchers conducted studies in various countries or in some geographical areas on the determinant of non-performing loans. The results of the findings are presented in the literature review. Therefore, at the best of our knowledge, no study has been conducted including many countries to compare whether or not factors affecting non-performing loans remain the same in space and time. Due to this knowledge gap, this study addresses this knowledge gap by including many countries from different areas so as to identify their determinant of non-performing loans and analyses their variability in the time.
Summary of Chapters
1. Introduction: This chapter defines the main objective of the research, which is to investigate the impact of economic indicators on non-performing loans, and outlines the problem statement regarding the impact of NPLs on economic growth.
2. Literature Review: This section provides a comprehensive overview of existing studies and findings regarding the micro-economic and macroeconomic determinants of non-performing loans globally.
3. Data and Methodology: This chapter describes the population and sample selection of countries, explains the usage of robust regression with R software, and details the description of the independent and dependent variables.
4. Empirical results: This section presents the findings from the robust regression analyses for the years 2015, 2016, and 2017, highlighting the significant determinants of NPLs and their changing impact over time.
Keywords
Inflation, Population growth, Unemployment rate, Interest rate, Domestic credit to private sector, Current balance account, Bank capital asset ratio, Robust Regression, Claims on private sector, Strength of legal index, Foreign direct investment, Gross domestic product growth
Frequently Asked Questions
What is the core focus of this research paper?
The research fundamentally investigates the relationship between various economic indicators and the incidence of non-performing loans (NPLs) across a large set of countries over the period from 2015 to 2017.
What are the central thematic areas covered in this study?
The study centers on bank-specific micro-economic factors and macroeconomic indicators, analyzing their influence on credit risk and how these relationships fluctuate or remain stable over time.
What is the primary research question being addressed?
The primary research aim is to determine the impact of specific economic indicators on NPLs and to establish whether the factors affecting non-performing loans remain constant or change on an annual basis.
Which scientific methodology is employed to reach the findings?
The author employs a cross-sectional analysis using a robust regression approach implemented via R software, utilizing data retrieved from the World Bank for the years 2015, 2016, and 2017.
What content is addressed in the main body of the work?
The main body covers the theoretical background via a literature review, the methodological framework including model construction, and an empirical analysis of regression results combined with a discussion of these findings.
Which keywords characterize the essence of this work?
Key terms include Non-performing loans (NPLs), Robust Regression, Inflation, GDP growth, Domestic credit to private sector, Current account balance, and Bank capital asset ratio.
How do the determinants of NPLs change between 2015 and 2017 according to the study?
The study finds that the number of influential economic factors increased over the period; while only a few factors were significant in 2015, by 2017, variables like population growth and GDP growth also showed significant impacts.
What is the practical recommendation for commercial banks?
The author recommends that banks develop credit risk assessment models that combine both internal bank factors and customer-specific factors to better assess repayment capacity and enhance customer selection.
- Quote paper
- Antoine Niyungeko (Author), 2020, Non-performing loans between 2015 and 2017. The impact of economic indicators, Munich, GRIN Verlag, https://www.hausarbeiten.de/document/902029
