Non-Performing Loans as a Driver of Banking Distress: A Systematic Literature Review
Abstract
:1. Introduction
2. Definition and Regulatory Landscape of NPLs
- Whether or not restructured loans need to be recognized as NPLs,
- Whether collateral is considered when granting a loan,
- Whether NPLs are listed as fully or partially overdue in terms of outstanding value,
- Whether banks must downgrade every loan.
3. Research Methodology
Inclusion Criteria
4. Literature Search and Evaluation
4.1. Determinants of NPLs: Theoretical Perspectives and Review from the Literature
4.1.1. Bank-Specific Factors
4.1.2. Industry Factors
4.1.3. Macroeconomic Factors
4.1.4. Health Crisis (COVID-19)
5. Analysis and Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Author (s) | Method | Macroeconomic Factors | Industry-Specific | Bank-Specific Factors | Country and Period of Study | Key Findings |
---|---|---|---|---|---|---|
Keeton and Morris (1987) [27] | Dynamic panel data | X | X | USA (1979–1985) | The results suggest that local economic conditions and bad sector performance are reasons for higher NPLs. | |
Sinkey and Greenawalt (1989) [30] | Log-linear regression model | X | X | USA (1984–1987) | The findings reveal that loan-loss rates are positively associated with loan rates. However, banks with adequate capital tend to have lower loss rates. | |
Berger and DeYoung (1997) [14] | Granger causality approach | X | USA (1985–1994) | Cost efficiency plays a key role in future bad loans. | ||
Kwan and Eisenbeis (1997) [25] | Simultaneous equations model | X | USA (1986–1995) | A U-shaped relationship is detected between inefficiency and loan growth. | ||
Keeton (1999) [17] | Surveys and reports | X | USA (1982–1996) | The results suggest that business loan growth and business credit change in loan growth are not always due to shifts in supply. | ||
Fernández et al. (2000) [73] | Government and bank reports | X | X | Spain (1963–1999) | During a boom period, credit disbursement is high; loans are given without considering the quality of loans. Thus, in a downturn period, NPLs will increase. | |
Salas and Saurina (2002) [26] | Panel data approach | X | X | Spain (1985–1997) | GDP, firms and family indebtedness, rapid past credit, inefficiency, size, net interest margin, and market power are major variables that explain NPLs. | |
Kalirai and Scheicher (2002) [74] | Dynamic panel data analysis (OLS) | X | Austria (1990–2001) | A rise in the short rate, a fall in business confidence, and a decline in the stock market have effects on loan loss provision, which in turn affect NPLs. | ||
Jiménez and Saurina (2002) [75] | Dynamic model | X | Spain (1984–2003) | During a boom period, bank managers tend to lend excessively despite herd behavior and agency problems. | ||
Nishimura and Kawamoto (2003) [76] | Government reports | X | Japan (1990–2000) | The study suggests that a major fragment that is given during the economic boom becomes bad loans as the economy shows a receding trend. | ||
Rajan and Dhal (2003) [32] | Panel regression model | X | X | Indian banks in 2003 | The empirical results suggest that NPLs are influenced by terms of credit, bank size, and macroeconomic factors. | |
Shih (2004) [77] | Interviews, policies, internal data | X | X | China (2004) | The findings strongly suggest that political considerations play a significant role in shaping financial policies in China. | |
Hu et al. (2004) [33] | Dynamic panel data GMM/OLS estimates | X | Taiwan (1996–1999) | The results show that NPLs decrease the ratio of government shareholding, and bank size is negatively related to NPLs. In addition, banks established after deregulation have a lower rate of NPLs. | ||
Girardone et al. (2004) [78] | Fourier-flexible stochastic | X | Italy (1993–1996) | Inefficiencies appear to be inversely associated with capital strength and positively correlated with NPLs. | ||
Babouček and Jančar (2005) [79] | Impulsive response and unrestricted VAR | X | Czech Republic (1993–2004) | The results reveal that both external stability and price stability are compatible with banking sector stability. Due increase NPLs will cause a rising trade deficit. In addition to that, a rise in NPLs will mitigate the growth of the unemployment rate. | ||
Lu et al. (2005) [80] | Logit regression | X | China (1994–1999) | The empirical findings suggest that Chinese banks have a systematic lending bias in favor of state-owned enterprises. | ||
Ghosh (2005) [81] | Fixed effects and dynamic GMM estimations | X | X | India (1993–2004) | The findings suggest that lagged leverage is an important indicator of bad loans of banks. | |
Fofack (2005) [7] | Econometric and causality analysis | X | X | Sub-Saharan Africa (1990) | The findings suggest that there is a dramatic and significant difference between Central African countries and Non-Central African countries. In addition to that, there is a strong causality between bad loans and macroeconomic factors. | |
Jimenez and Saurina (2006) [49] | GMM estimations | X | Spain (1984–2002) | The empirical results find dedicated support for a positive relationship between rapid credit growth and loan loss. Moreover, three is robust evidence that during the boom period, risker borrowers get bank loans while collateralized loans decrease. | ||
Rinaldi and Sanchis-Arellano (2006) [59] | Unbalanced and balanced estimation | X | EU countries (1989–2004) | The findings suggest that the financial conditions of households might become more vulnerable to adverse shocks in their income. | ||
Berge and Boye (2007) [4] | Panel regression | X | Nordic banking sector (1993–2005) | The results reveal that the decline of NPLs is primarily attributed to the development in the real interest rate and unemployment. | ||
Quagliarello (2007) [82] | Static and dynamic model | X | X | Italy (1985–2002) | The findings confirm the business cycle affects banks’ loan loss provision and new bad debt. | |
Podpiera and Weill (2008) [18] | GMM dynamic panel | X | X | Czech Republic (1994–2005) | The findings support the bad management hypothesis in which the deterioration of cost efficiency led to increasing in NPLs. | |
Rossi et al. (2009) [19] | Granger causality approach | X | X | Austria (1997–2003) | The empirical results find that, although diversification is negatively correlated with cost efficiency, it increases profit efficiency and reduces banks’ credit risk. | |
Boudriga et al. (2010) [83] | Pooled regression approach | X | X | MENA (2002–2006) | The results find that among bank-specific factors, higher credit growth and foreign participation reduce NPLs. Additionally, the role of the institutional environment in enhancing bank credit quality. Better control of corruption, sound regulatory quality, better enforcement of the rule of law, and accountability play a significant role in reducing NPLs in the MENA region. | |
Barseghyan (2010) [84] | Two-period overlapping generations (OLG) | X | X | Japan (1990–2003) | The existence of NPLs, combined with a delay in the bailout, leads to a persistent decline in economic activity. | |
Espinoza et al. (2010) [6] | System GMM, panel vector autoregressive | X | X | MENA—GCC countries (1995–2008 | The study supports the view that both macro- and bank-specific factors play a key role in determining NPLs. | |
Reinhart and Rogoff (2011) [68] | Vector autoregression | X | Global (1800–2009) | The study examines a sample of 290 banking crises and 209 sovereign episodes. Banking crises are importantly preceded by increasing private indebtedness. | ||
Nkusu (2011) [56] | Panel regression and panel vector autoregressive | X | X | Global (1998–2009) | The findings confirm that adverse macroeconomic development is associated with rising of NPLs. | |
Agoraki et al. (2011) [44] | Dynamic model | X | X | X | Central and Eastern European countries (1998–2005) | The empirical results reveal that banks with market power tend to take on lower credit risk and have a lower probability of default. |
De Bock and Demyanets (2012) [52] | Dynamic panel regression | X | X | Emerging countries (1996–2010) | The results find a significant link between banks’ asset quality, credit, and macroeconomic aggregates. Economic activity slows down when NPLs increase. | |
Louzis et al. (2012) [20] | Dynamic panel regression | X | X | Greece (2003–2009) | NPLs in Greece can be explained mainly by macroeconomic and management quality. | |
Messai and Jouini (2013) [85] | Panel data regression | X | X | EU (2004–2008) | The findings suggest that GDP growth and ROA have a negative impact on NPLs. The unemployment rate and the real interest rate have a positive effect on NPLs. | |
Klein (2013) [29] | Dynamic panel regression | X | X | EU (1998–2011) | The results find that NPLs can be attributed to both macroeconomic and bank-specific factors. Interestingly, the bank-level effects were significant during pre-crisis and post-crisis. | |
Castro (2013) [86] | Dynamic panel regression | X | EU—GIPSI countries (1997q1–2011q3) | The banking credit risk is significantly affected by the macroeconomic environment. In addition to the global financial crisis, several robustness tests confirmed the results. | ||
Makri et al. (2014) [55] | Dynamic panel regression | X | X | EU (2000–2008) | The results confirm a strong relation between NPLs and macroeconomic factors (public debt, unemployment, growth rate of GDP) and bank-specific factors (CAR, ROE, rate of NPLs of the previous year). | |
Ghosh (2015) [34] | Fixed effect and dynamic GMM | X | X | USA (1984–2013) | Greater capitalization, liquidity risks, poor credit quality, greater cost inefficiency, and bank size significantly increase NPLs. On the contrary, higher GDP and changes in state housing prices lower NPLs. | |
Fu et al. (2014) [45] | Panel data model | X | X | X | Asian pacific economies (2003–2010) | The results suggest that greater concentration fosters financial fragility and that lower pricing levels will induce bank exposure. |
Beck et al. (2015) [50] | Dynamic panel regression | X | Global (2000–2010) | Real GDP growth, share prices, exchange rates, and lending interest rates significantly affected NPLs. | ||
Baselga-Pascual et al. (2015) [87] | System GMM estimator | X | X | Euro area (2001–2012) | Capitalization, profitability, efficiency, and liquidity are inversely related to bank risk. Less -concentrated markets, lower interest rates, higher inflation, and falling GDP increase bank risk | |
Touny and Shehab (2015) [88] | Dynamic panel regression | X | Arab countries (2000–2012) | The findings suggest that the inflation rate, improvement in macroeconomic and financial conditions, and the global financial crisis have a significant negative relation with NPLs. While household consumption found a negative impact in non-petroleum countries, petroleum countries had a positive effect. | ||
Chaibi and Ftiti (2015) [89] | GMM estimations | X | X | France and Germany (2005–2011) | The results indicate that expectations for inflation rates in both counties are influenced by a set of macroeconomic factors used in the paper. Additionally, the findings reveal that France is more susceptible to bank-specific than Germany. | |
Rajha (2016) [35] | Panel data regression | X | X | Jordan (2008–2012) | Among other bank-specific factors, the lagged NPLs and the ratio of loans to total assets are the most crucial factors that affect NPLs. Regarding macroeconomic factors, economic growth and inflation have negative influences on NPLs. Along with this, the global financial crisis is positively correlated with NPLs. | |
Agarwal et al. (2016) [90] | Logistic model | X | USA (2003–2007) | Condominium loan defaults grow at a faster rate. The greater default level and growth rate are consistent with investor expectations. | ||
González (2017) [91] | Panel data estimates. OLS, fixed effect, and random effect | X | X | X | MENA (2005–2012) | The findings suggest the U-shaped relation between banks’ competition and banks’ risk-taking. Both competition-stability and competition-fragility can be applied at the same time in the MENA region. |
Amuakwa-Mensah et al. (2017) [92] | Dynamic, state, and impulse response analysis | X | X | Ghana (1997–2011) | NPLs are significantly affected by both bank-specific factors and macroeconomic factors. The role of the global financial crisis was observed to be conditional on NPLS. | |
Ghosh (2017) [58] | Static and dynamic estimation | X | USA (1992q4–2016q1) | Total NPLs have the most effect on US housing prices and real GDP growth. At disaggregate levels, non-performing construction, land development, and C&I loans have the most persistent impact on sector-specific growth. | ||
Bashir et al. (2017) [69] | Two-step system GMM dynamic panel | X | X | X | China (2000–2014) | The high transparency in the Chinese banking sector reduces poor-quality assets, but not in the case of state-owned banks, while an increase in competition increases NPLs. |
Kjosevski and Petkovski (2017) [93] | Panel data analysis | X | X | Baltic states (2005–2014) | The main determinants that influence NPLs were, among others, macroeconomic and bank-specific factors. | |
Ghosh (2018) [70] | 3SLS method | X | MENA (2001–2012) | The findings confirm the bad luck hypothesis and the gamble for resurrection hypothesis to be equally relevant. Hoverer, this behavior is different between oil-exporters and oil-importers countries. | ||
Ghosh (2018) [94] | Panel data fixed effects estimation | X | X | USA (1999Q1–2016Q3) | Greater regulatory capital, more diversification, higher profits, and cost efficiency reduce the charge-off rate. On the contrary, a higher share of loans and a higher share of real estate loans have a significant impact on loan performance. Strong macroeconomics reduces loan charge-offs. | |
Vo (2018) [95] | Dynamic estimation technique | X | X | X | Vietnam (2006–2015) | The findings suggest that bank lending behavior is significantly influenced by both bank-specific factors and macroeconomic factors. |
Cui et al. (2018) [96] | Panel regression techniques | X | China (2009–2015) | The results find that green loans to total loans do reduce banks’ NPLs. | ||
Koju et al. (2018) [97] | Static and dynamic panel estimation | X | X | Nepal (2003–2015) | NPLs have a significant positive relation with bank size, inefficiency, and export-to-import ratio while a negative relation with GDP, CAR, and inflation. | |
Bapat (2018) [98] | Dynamic panel data analysis | X | X | India (2006/2007–2012/2013) | Among others, NPLs and the cost-to-income ratio negatively affect bank profitability. | |
Jabbouri and Naili (2019) [99] | Panel data analysis | X | X | MENA (2003–2016) | The results show that bank size, capital adequacy ratio, bank operation efficiency profitability, GDP growth, unemployment, inflation, and public debt represent the main determinants of NPLs. | |
Bayar (2019) [100] | GMM dynamic panel | X | X | Emerging countries (2000–2013) | The findings reveal that economic growth, inflation, institutional quality, ROA, ROE, CAR, and non-interest income affect NPLs negatively, while unemployment, public debt, credit growth, lagged value of NPLs, cost-to-income ratio, and global financial crisis affect NPLs positively. | |
Rachid (2019) [101] | Panel data analysis | X | X | MENA and CEE countries (1997–2016) | The empirical results find that rule of law increases NPLs in MENA while decreasing NPLs in CEE. Moreover, the global financial crisis has a significant role in the accumulation of NPLs in MENA countries. | |
Gulati et al. (2019) [102] | System GMM approach | X | X | X | India (1998/99–2014/15) | Lower profitability, more diversification, large size, and higher concentration increase the probability of default in the Indian banking system. |
Kuzucu and Kuzucu (2019) [103] | Dynamic panel estimations | X | X | Emerging and Advanced economies (2001–2015) | Real GDP growth is the main determinant that affects NPLs, and NPLs exhibit high persistence for both emerging and advanced economies in pre-crisis and post-crisis | |
Farooq et al. (2019) [104] | Two-step GMM | X | X | GCC countries (2009–2015) | The empirical findings generated from the bad management hypothesis, bad luck, asset size, and combined theories are statistically significant. | |
Ghosh (2019) [105] | Regression analysis | X | X | X | MENA (2000–2012) | The results suggest credit reporting system reforms lead to a decline NPLs. Among others, the efficiency of credit reporting systems is much less compelling during the crisis. |
Anastasiou et al. (2019) [106] | OLS and Bayesian panel-cointegration vector autoregression | X | X | Euro area (2003q1–2016q1) | NPLs in the Euro area have performed an upward shift after 2008 and are mostly related to worsening macroeconomic conditions. | |
Mahrous et al. (2020) [107] | Dynamic panel GMM and threshold | X | MENA (1997–2017) | The findings indicate the relationship between monetary policy and credit risk is positive and significant to a certain threshold 6.3. | ||
Betz et al. (2020) [108] | Survival analysis | X | Sample of defaulted bank loans in USA, Great Britain, and Canada | Frailties have a huge impact on the resolution time of NPLs. Moreover, the findings suggest that the resolution of NPLs is a key determinant of bank credit default losses. | ||
Gupta et al. (2020) [109] | Fixed effect and GMM | X | X | X | India (1999–2016) | The main findings suggest that private banks are more capitalized and operate more efficiently than public banks. |
Boussaada et al. (2020) [110] | PSTR model | X | X | MENA (2004–2017) | The results suggest that there is a threshold effect on liquidity risk and NPLs. | |
Ahmed et al. (2021) [111] | System GMM estimation | X | X | Pakistan (2008–2018) | Credit growth, net interest margin, loan loss provision, and bank diversification increase NPLs while operating efficiency, bank size, and ROA reduce NPLs. Regarding macroeconomic factors, GDP decreases NPLs while interest rate, exchange rate, and political risk increase NPLs. | |
Katusiime (2021) [112] | ARDL approach | X | X | Uganda (2000q1–2021q1) | The findings suggest that COVID-19 has a significant negative effect only in the long run. In the short-run bank, profitability negatively affects NPLs, liquidity ratio, and market sensitivity. | |
Kılıç Depren and Kartal (2021) [113] | Predictive analysis, multivariate adaptive regression splines | X | X | Turkey (2005–2019) | The results find that credits, the US dollar to Turkish lira exchange rate, and unemployment are the most significant factors in defining NPLs. | |
Alnabulsi and Kozarevic (2021) [114] | Multiple linear regression model | X | Jordan (2006–2019) | The findings suggest that for economic growth factors such as GDP growth and unemployment, there is a negative relation with NPLs. While for financial stability indicators such as lending interest rate and capital adequacy ratio, there is a positive relation with NPLs. | ||
Syed and Aidyngul (2022) [115] | Dynamic GMM technique | X | X | Developing and developed countries (1995–2019) | The common macroeconomic and bank-specific affect NPLs among both developed and developing countries. | |
Taghizadeh-Hesary et al. (2022) [116] | Vector autoregressive approach | X | X | ASEAN member states (pre-COVID and post-COVID) | The empirical results prove that the loan default ratio is the optimal credit guarantee ratio’s main indicator. In addition, in the ASEAN region, the credit guarantee needs to be increased to help SMEs in the wake of COVID-19. | |
Abusharbeh (2022) [117] | Fixed and random effect estimates | X | X | Palestine (2007–2018) | The results of the fixed effect prove that interest and credit supply are positively correlated with NPLs, while profitability has significant negative relation with NPLs. | |
Naili and Lahrichi (2022) [118] | Dynamic GMM technique | X | X | MENA (2000–2019) | The results suggest that GDP growth, unemployment, bank capitalization, bank performance, bank operating inefficiency, bank concentration, inflation, sovereign debt, and bank size are the main factors that affect NPLs. | |
Alnabulsi et al. (2022) [47] | System generalized method of moment | X | X | X | MENA (2005–2020) | The empirical findings suggest that NPLs are more sensitive to bank-specific factors than macroeconomic factors. In addition, the financial environment and institutional quality significantly affect NPLs. |
References
- Cingolani, M. Finance capitalism: A look at the European financial accounts. Panoeconomicus 2013, 60, 249–290. [Google Scholar] [CrossRef]
- Saunders, A.; Corner, M. Financial Institutions Management: A Risk Management Approach, 8th ed.; McGraw-Hill Education: New York, NY, USA, 2008. [Google Scholar]
- Samad, A. Credit risk determinants of bank failure: Evidence from US bank failure. Int. Bus. Res. 2012, 5, 10–15. [Google Scholar] [CrossRef]
- Berge, O.; Boye, G. An analysis of banks’ problem loans. Econ. Bull. 2007, 2, 65–76. [Google Scholar]
- Umar, M.; Sun, G. Determinants of non-performing loans in Chinese banks. J. Asia Bus. Stud. 2018, 12, 273–289. [Google Scholar] [CrossRef]
- Espinoza, R.; Prasad, A. Nonperforming Loans in the GCC Banking Systems and Their Macroeconomic Effects; IMF Working Paper 10/224; International Monetary Fund: Washington, DC, USA, 2010. [Google Scholar]
- Fofack, H. Nonperforming Loans in Sub-Saharan Africa: Causal Analysis and Macroeconomic Implications; Working Paper No. 3769; World Bank Policy Research: Washington, DC, USA, 2005. [Google Scholar]
- Vom Brocke, J.; Simons, A.; Niehaves, B.; Niehaves, B.; Reimer, K.; Plattfaut, R.; Cleven, A. Reconstructing the giant: On the importance of rigour in documenting the literature search process. In Proceedings of the ECIS 2009, Verona, Italy, 8–10 June 2009. [Google Scholar]
- Nikolopoulos, K.I.; Tsalas, A.I. Non-performing Loans: A Review of the Literature and the International Experience. In Non-Performing Loans and Resolving Private Sector Insolvency: Experiences from the EU Periphery and the Case of Greece; Monokroussos, P., Gortsos, C., Eds.; Springer: Cham, Stwitzerland, 2017; pp. 47–68. [Google Scholar] [CrossRef]
- Manz, F. Determinants of non-performing loans: What do we know? A systematic review and avenues for future research. Manag. Rev. Q. 2019, 69, 351–389. [Google Scholar] [CrossRef]
- Krueger, R. International Standards for Impairment and Provisions and Their Implications for Financial Soundness Indicators (FSIs); International Monetary Fund: Washington, DC, USA, 2002. [Google Scholar]
- International Monetary Fund. Financial Soundness Indicators: Compilation Guide; International Monetary Fund: Washington, DC, USA, 2006; ISBN 978-1-58906-385-3. [Google Scholar]
- BCBS International Convergence of Capital Measurement and Capital Standards: A Revised Framework Comprehensive Version; Bank for International Settlements: Basel, Switzerland, 2006; Available online: https://www.bis.org/publ/bcbs128.htm (accessed on 19 January 2023).
- Berger, A.; DeYoung, R. Problem loans and cost efficiency in commercial banks. J. Bank. Financ. 1997, 21, 849–870. [Google Scholar] [CrossRef] [Green Version]
- Angklomkliew, S.; George, J.; Packer, F. Issues and Developments in Loan Loss Provisioning: The Case of Asia. BIS Q. Rev. 2009, 69–83. Available online: https://ssrn.com/abstract=1519809 (accessed on 19 January 2023).
- Bown, M.; Sutton, A. Quality control in systematic reviews and meta-analyses. Eur. J. Vasc. Endovasc. Surg. 2010, 40, 669–677. [Google Scholar] [CrossRef] [Green Version]
- Keeton, W. Does faster loan growth lead to higher loan losses? Econ. Rev. Fed. Reserve Bank Kans. City 1999, 84, 57–75. [Google Scholar]
- Podpiera, J.; Weill, L. Bad luck or bad management? Emerging banking market experience. J. Financ. Stab. 2008, 4, 135–148. [Google Scholar] [CrossRef] [Green Version]
- Rossi, S.; Schwaiger, M.; Winkler, G. How loan portfolio diversification affects risk, efficiency and capitalization: A managerial behavior model for Austrian banks. J. Bank. Financ. 2009, 33, 2218–2226. [Google Scholar] [CrossRef]
- Louzis, D.; Vouldis, A.; Metaxas, V. Macroeconomic and bank-specific determinants of non-performing loans in Greece: A comparative study of mortgage, business and consumer loan portfolios. J. Bank. Financ. 2012, 36, 1012–1027. [Google Scholar] [CrossRef]
- Dimitrios, A.; Helen, L.; Mike, T. Determinants of non-performing loans: Evidence from Euro-area countries. Financ. Res. Lett. 2016, 18, 116–119. [Google Scholar] [CrossRef]
- Konstantakis, K.; Michaelides, P.; Vouldis, A. Non performing loans (NPLs) in a crisis economy: Long-run equilibrium analysis with a real time VEC model for Greece (2001–2015). Phys. A Stat. Mech. Its Appl. 2016, 451, 149–161. [Google Scholar] [CrossRef]
- Berg, S.; Førsund, F.; Jansen, E. Malmquist indices of productivity growth during the deregulation of Norwegian banking, 1980–89. Scand. J. Econ. 1992, 94, S211–S228. [Google Scholar] [CrossRef]
- Hughes, J.; Mester, L. A quality and risk-adjusted cost function for banks: Evidence on the “too-big-to-fail” doctrine. J. Product. Anal. 1993, 4, 293–315. [Google Scholar] [CrossRef]
- Kwan, S.; Eisenbeis, R. Bank risk, capitalization, and operating efficiency. J. Financ. Serv. Res. 1997, 12, 117–131. [Google Scholar] [CrossRef]
- Salas, V.; Saurina, J. Credit risk in two institutional regimes: Spanish commercial and savings banks. J. Financ. Serv. Res. 2002, 22, 203–224. [Google Scholar] [CrossRef]
- Keeton, R.; Morris, S. Why do banks’ loan losses differ. Econ. Rev. 1987, 72, 3–21. [Google Scholar]
- Goczek, Ł.; Malyarenko, N. Loan loss provisions during the financial crisis in Ukraine. Post-Communist Econ. 2015, 27, 472–496. [Google Scholar] [CrossRef]
- Klein, N. Non-Performing Loans in CESEE: Determinants and Impact on Macroeconomic Performance; IMF Working Paper WP/13/72; International Monetary Fund: Washington, DC, USA, 2013. [Google Scholar]
- Sinkey, J.; Greenawalt, M. Loan-loss experience and risk-taking behavior at large commercial banks. J. Financ. Serv. Res. 1991, 5, 43–59. [Google Scholar] [CrossRef]
- Us, V. Dynamics of non-performing loans in the Turkish banking sector by an ownership breakdown: The impact of the global crisis. Financ. Res. Lett. 2017, 20, 109–117. [Google Scholar] [CrossRef]
- Rajan, R.; Dhal, S. Non-performing loans and terms of credit of public sector banks in India: An empirical assessment. Reserve Bank India Occas. Pap. 2003, 24, 81–121. [Google Scholar]
- Hu, J.; Li, Y.; Chiu, Y. Ownership and nonperforming loans: Evidence from Taiwan’s banks. Dev. Econ. 2004, 42, 405–420. [Google Scholar] [CrossRef]
- Ghosh, A. Banking-industry specific and regional economic determinants of non-performing loans: Evidence from US states. J. Financ. Stab. 2015, 20, 93–104. [Google Scholar] [CrossRef]
- Rajha, K.S. Determinants of non-performing loans: Evidence from the Jordanian banking sector. J. Financ. Bank Manag. 2016, 4, 125–136. [Google Scholar] [CrossRef] [Green Version]
- Swamy, V. Impact of macroeconomic and endogenous factors on nonperforming banks assets. Int. J. Bank. Financ. 2012, 9, 26–47. [Google Scholar] [CrossRef] [Green Version]
- García-Marco, T.; Robles-Fernández, M. Risk-taking behaviour and ownership in the banking industry: The Spanish evidence. J. Econ. Bus. 2008, 60, 332–354. [Google Scholar] [CrossRef]
- Keeley, M. Deposit insurance, risk, and market power in banking. Am. Econ. Rev. 1990, 80, 1183–1200. [Google Scholar]
- Matutes, C.; Vives, X. Imperfect competition, risk taking, and regulation in banking. Eur. Econ. Rev. 2000, 44, 1–34. [Google Scholar] [CrossRef]
- Hellmann, T.; Murdock, K.; Stiglitz, J. Liberalization, moral hazard in banking, and prudential regulation: Are capital requirements enough? Am. Econ. Rev. 2000, 90, 147–165. [Google Scholar] [CrossRef] [Green Version]
- Allen, F.; Gale, D. Competition and financial stability. J. Money Credit Bank. 2004, 36, 453–480. [Google Scholar] [CrossRef] [Green Version]
- Boyd, J.; De Nicolo, G. The theory of bank risk taking and competition revisited. J. Financ. 2005, 60, 1329–1343. [Google Scholar] [CrossRef]
- Schaeck, K.; Cihak, M.; Wolfe, S. Are competitive banking systems more stable? J. Money Credit Bank. 2009, 41, 711–734. [Google Scholar] [CrossRef] [Green Version]
- Agoraki, M.; Delis, M.; Pasiouras, F. Regulations, competition and bank risk-taking in transition countries. J. Financ. Stab. 2011, 7, 38–48. [Google Scholar] [CrossRef] [Green Version]
- Fu, X.; Lin, Y.; Molyneux, P. Bank competition and financial stability in Asia Pacific. J. Bank. Financ. 2014, 38, 64–77. [Google Scholar] [CrossRef]
- Kasman, S.; Kasman, A. Bank competition, concentration and financial stability in the Turkish banking industry. Econ. Syst. 2015, 39, 502–517. [Google Scholar] [CrossRef]
- Alnabulsi, K.; Kozarević, E.; Hakimi, A. Assessing the determinants of non-performing loans under financial crisis and health crisis: Evidence from the MENA banks. Cogent Econ. Financ. 2022, 10, 2124665. [Google Scholar] [CrossRef]
- Bernanke, B.; Gertler, M. Agency Costs, Net Worth, and Business Fluctuations. Am. Econ. Rev. 1989, 79, 14–31. [Google Scholar]
- Jimenez, G.; Saurina, J. Credit cycles, credit risk, and prudential regulation. Int. J. Cent. Bank. 2006, 2, 65–98. [Google Scholar]
- Beck, R.; Jakubik, P.; Piloiu, A. Key determinants of non-performing loans: New evidence from a global sample. Open Econ. Rev. 2015, 26, 525–550. [Google Scholar] [CrossRef]
- Carey, M. Credit risk in private debt portfolios. J. Financ. 1998, 53, 1363–1387. [Google Scholar] [CrossRef]
- De Bock, M.; Demyanets, M. Bank Asset Quality in Emerging Markets: Determinants and Spillovers; International Monetary Fund: Washington, DC, USA, 2012. [Google Scholar]
- Bofondi, M.; Ropele, T. Macroeconomic Determinants of Bad Loans: Evidence from Italian Banks’; Bank of Italy Occasional Paper; Bank of Italy: Rome, Italy, 2011. [Google Scholar]
- Moinescu, B.G. Determinants of nonperforming loans in Central and Eastern European Countries: Macroeconomic indicators and credit discipline. Rev. Econ. Bus. Stud. 2012, 10, 47–58. [Google Scholar]
- Makri, V.; Tsagkanos, A.; Bellas, A. Determinants of non-performing loans: The Case of Eurozone. Panoeconomicus 2014, 61, 193–206. [Google Scholar] [CrossRef] [Green Version]
- Nkusu, M. Non-Performing Loans and Macro-Financial Vulnerabilities in Advanced Economies; IMF Working Paper 11/161; International Monetary Fund: Washington, DC, USA, 2011. [Google Scholar]
- Zhang, D.; Cai, J.; Dickinson, D.; Kutan, A. Non-performing loans, moral hazard and regulation of the Chinese commercial banking system. J. Bank. Financ. 2016, 63, 48–60. [Google Scholar] [CrossRef]
- Ghosh, A. Sector-specific analysis of non-performing loans in the US banking system and their macroeconomic impact. J. Econ. Bus. 2017, 93, 29–45. [Google Scholar] [CrossRef]
- Rinaldi, L.; Sanchis-Arellano, A. Household Debt Sustainability: What Explains Household Nonperforming Loans? An Empirical Analysis; ECB Working Paper Series No. 570; European Central Bank: Frankfurt am Main, Germany, 2006. [Google Scholar]
- Reinhart, C. From health crisis to financial distress. IMF Econ. Rev. 2022, 70, 4–31. [Google Scholar] [CrossRef]
- Aiyar, S.; Bergthaler, W.; Garrido, J.M.; Ilyina, A.; Jobst, A.; Kang, K.; Kovtun, D.; Liu, Y.; Monaghan, D.; Moretti, M. A strategy for resolving Europe’s problem loans. Eur. Econ. 2017, 1, 87–95. [Google Scholar] [CrossRef]
- Accornero, M.; Alessandri, P.; Carpinelli, L.; Sorrentino, A.M. Non-Performing Loans and the Supply of Bank Credit: Evidence from Italy; Bank of Italy Occasional Paper; Bank of Italy: Rome, Italy, 2017. [Google Scholar] [CrossRef] [Green Version]
- Žunić, A.; Kozarić, K.; Dželihodžić, E.Ž. Non-performing loan determinants and impact of covid-19: Case of Bosnia and Herzegovina. J. Cent. Bank. Theory Pract. 2021, 10, 5–22. [Google Scholar] [CrossRef]
- Ari, A.; Chen, S.; Ratnovski, L. The dynamics of non-performing loans during banking crises: A new database with post-COVID-19 implications. J. Bank. Financ. 2021, 133, 106140. [Google Scholar] [CrossRef]
- Apergis, N. Convergence in non-performing loans across EU banks: The role of COVID-19. Cogent Econ. Financ. 2022, 10, 2024952. [Google Scholar] [CrossRef]
- Beck, T.; Keil, J. Have banks caught corona? Effects of COVID on lending in the US. J. Corp. Financ. 2022, 72, 102160. [Google Scholar] [CrossRef]
- Dursun-de Neef, H.Ö.; Schandlbauer, A. COVID-19 and lending responses of European banks. J. Bank. Financ. 2021, 133, 106236. [Google Scholar] [CrossRef] [PubMed]
- Reinhart, C.; Rogoff, K. From financial crash to debt crisis. Am. Econ. Rev. 2011, 101, 1676–1706. [Google Scholar] [CrossRef] [Green Version]
- Bashir, U.; Yu, Y.; Hussain, M.; Wang, X.; Ali, A. Do banking system transparency and competition affect nonperforming loans in the Chinese banking sector? Appl. Econ. Lett. 2017, 24, 1519–1525. [Google Scholar] [CrossRef]
- Ghosh, S. Bad luck, Bad policy or Bad banking? Understanding the financial management behavior of MENA banks. J. Multinatl. Financ. Manag. 2018, 47, 110–128. [Google Scholar] [CrossRef]
- Juodis, A.; Karavias, Y.; Sarafidis, V. A homogeneous approach to testing for Granger non-causality in heterogeneous panels. Empir. Econ. 2021, 60, 93–112. [Google Scholar] [CrossRef]
- Ditzen, J.; Karavias, Y.; Westerlund, J. Testing and estimating structural breaks in time series and panel data in stata. arXiv 2021, arXiv:2110.14550. [Google Scholar]
- Abusharbeh, M.T. Determinants of credit risk in Palestine: Panel data estimation. Int. J. Financ. Econ. 2022, 27, 3434–3443. [Google Scholar] [CrossRef]
- Agarwal, S.; Deng, Y.; Luo, C.; Qian, W. The hidden peril: The role of the condo loan market in the recent financial crisis. Rev. Financ. 2016, 20, 467–500. [Google Scholar] [CrossRef]
- Ahmed, S.; Majeed, M.E.; Thalassinos, E.; Thalassinos, Y. The impact of bank specific and macro-economic factors on non-performing loans in the banking sector: Evidence from an emerging economy. J. Risk Financ. Manag. 2021, 14, 217. [Google Scholar] [CrossRef]
- Alnabulsi, K.; Kozarević, E. Interdependence between non-performing loans, financial stability and economic growth. In Proceedings of the 10th International Scientific Symposium “Region, Entrepreneurship, Development” (RED 2021), Osijek, Croatia, 17 June 2021; Leko Šimić, M., Crnković, B., Eds.; 2021. Available online: http://www.efos.unios.hr/red/wp-content/uploads/sites/20/2021/07/RED_2021_Proceedings.pdf (accessed on 19 January 2023).
- Amuakwa-Mensah, F.; Marbuah, G.; Ani-Asamoah Marbuah, D. Re-examining the determinants of non-performing loans in Ghana’s banking industry: Role of the 2007–2009 financial crisis. J. Afr. Bus. 2017, 18, 357–379. [Google Scholar] [CrossRef]
- Anastasiou, D.; Louri, H.; Tsionas, M. Nonperforming loans in the euro area: A re core–periphery banking markets fragmented? Int. J. Financ. Econ. 2019, 24, 97–112. [Google Scholar] [CrossRef] [Green Version]
- Babouček, I.; Jančar, M. Effects of Macroeconomic Shocks to the Quality of the Aggregate Loan Portfolio (Vol. 22); Czech National Bank Working Paper Series 1; Czech National Bank: Praha, Czech, 2005. [Google Scholar]
- Bapat, D. Profitability drivers for Indian banks: A dynamic panel data analysis. Eurasian Bus. Rev. 2018, 8, 437–451. [Google Scholar] [CrossRef]
- Barseghyan, L. Non-performing loans, prospective bailouts, and Japan’s slowdown. J. Monet. Econ. 2010, 57, 873–890. [Google Scholar] [CrossRef]
- Baselga-Pascual, L.; Trujillo-Ponce, A.; Cardone-Riportella, C. Factors influencing bank risk in Europe: Evidence from the financial crisis. N. Am. J. Econ. Financ. 2015, 34, 138–166. [Google Scholar] [CrossRef]
- Bayar, Y. Macroeconomic, institutional and bank-specific determinants of non-performing loans in emerging market economies: A dynamic panel regression analysis. J. Cent. Bank. Theory Pract. 2019, 8, 95–110. [Google Scholar] [CrossRef] [Green Version]
- Betz, J.; Krüger, S.; Kellner, R.; Rösch, D. Macroeconomic effects and frailties in the resolution of non-performing loans. J. Bank. Financ. 2020, 112, 105212. [Google Scholar] [CrossRef]
- Boudriga, A.; Taktak, N.; Jellouli, S. Bank Specific, Business and Institutional Environment Determinants of Banks Nonperforming Loans: Evidence from Mena Countries; Working Paper 547; Economic Research Forum: Cario, Egypt, 2010; pp. 1–28. [Google Scholar]
- Boussaada, R.; Hakimi, A.; Karmani, M. Is there a threshold effect in the liquidity risk–non-performing loans relationship? A PSTR approach for MENA banks. Int. J. Financ. Econ. 2020, 27, 1886–1898. [Google Scholar] [CrossRef]
- Castro, V. Macroeconomic determinants of the credit risk in the banking system: The case of the GIPSI. Econ. Model. 2013, 31, 672–683. [Google Scholar] [CrossRef] [Green Version]
- Chaibi, H.; Ftiti, Z. Credit risk determinants: Evidence from a cross-country study. Res. Int. Bus. Financ. 2015, 33, 1–16. [Google Scholar] [CrossRef]
- Cui, Y.; Geobey, S.; Weber, O.; Lin, H. The impact of green lending on credit risk in China. Sustainability 2018, 10, 2008. [Google Scholar] [CrossRef] [Green Version]
- Farooq, M.O.; Elseoud, M.; Turen, S.; Abdulla, M. Causes of non-performing loans: The experience of gulf cooperation council countries. Entrep. Sustain. Issues 2019, 6, 1955–1974. [Google Scholar] [CrossRef]
- Fernández de Lis, S.; Martínez, J.; Saurina, J. Credit Growth, Problem Loans and Credit Risk Provisioning in Spain; Working Paper 18; Servicio de Estudios; Banco de España: Valencia, Spain, 2000. [Google Scholar]
- Ghosh, A. Determinants of bank loan charge-off rates: Evidence from the USA. J. Financ. Regul. Compliance 2018, 26, 526–542. [Google Scholar] [CrossRef]
- Ghosh, S. Does leverage influence banks’ non-performing loans? Evidence from India. Appl. Econ. Lett. 2005, 12, 913–918. [Google Scholar] [CrossRef]
- Ghosh, S. Loan delinquency in banking systems: How effective are credit reporting systems? Res. Int. Bus. Financ. 2019, 47, 220–236. [Google Scholar] [CrossRef]
- Girardone, C.; Molyneux, P.; Gardener, E. Analysing the determinants of bank efficiency: The case of Italian banks. Appl. Econ. 2004, 36, 215–227. [Google Scholar] [CrossRef] [Green Version]
- González, L.; Razia, A.; Búa, M.; Sestayo, R. Competition, concentration and risk taking in Banking sector of MENA countries. Res. Int. Bus. Financ. 2017, 42, 591–604. [Google Scholar] [CrossRef]
- Gulati, R.; Goswami, A.; Kumar, S. What drives credit risk in the Indian banking industry? An empirical investigation. Econ. Syst. 2019, 43, 42–62. [Google Scholar] [CrossRef]
- Gupta, N.; Mahakud, J. Ownership, bank size, capitalization and bank performance: Evidence from India. Cogent Econ. Financ. 2020, 8, 1808282. [Google Scholar] [CrossRef]
- Jabbouri, I.; Naili, M. Determinants of nonperforming loans in emerging markets: Evidence from the MENA region. Rev. Pac. Basin Financ. Mark. Policies 2019, 22, 1950026. [Google Scholar] [CrossRef]
- Jiménez, G.; Saurina, J. Loan Characteristics and Credit Risk; Bank of Spain: Valencia, Spain, 2002; pp. 1–34. [Google Scholar]
- Kalirai, H.; Scheicher, M. Macroeconomic stress testing: Preliminary evidence for Austria. Financ. Stab. Rep. 2002, 3, 58–74. [Google Scholar]
- Katusiime, L. COVID 19 and bank profitability in low income countries: The case of Uganda. J. Risk Financ. Manag. 2021, 14, 588. [Google Scholar] [CrossRef]
- Kılıç Depren, S.; Kartal, T. Prediction on the volume of non-performing loans in Turkey using multivariate adaptive regression splines approach. Int. J. Financ. Econ. 2021, 26, 6395–6405. [Google Scholar] [CrossRef]
- Kjosevski, J.; Petkovski, M. Non-performing loans in Baltic States: Determinants and macroeconomic effects. Balt. J. Econ. 2017, 17, 25–44. [Google Scholar] [CrossRef] [Green Version]
- Koju, L.; Koju, R.; Wang, S. Macroeconomic and bank-specific determinants of non-performing loans: Evidence from Nepalese banking system. J. Cent. Bank. Theory Pract. 2018, 7, 111–138. [Google Scholar] [CrossRef] [Green Version]
- Kuzucu, N.; Kuzucu, S. What drives non-performing loans? Evidence from emerging and advanced economies during pre-and post-global financial crisis. Emerg. Mark. Financ. Trade 2019, 55, 1694–1708. [Google Scholar] [CrossRef]
- Lu, D.; Thangavelu, S.; Hu, Q. Biased lending and non-performing loans in China’s banking sector. J. Dev. Stud. 2005, 41, 1071–1091. [Google Scholar] [CrossRef]
- Mahrous, S.; Samak, N.; Abdelsalam, M. The effect of monetary policy on credit risk: Evidence from the MENA region countries. Rev. Econ. Political Sci. 2020, 5, 289–304. [Google Scholar] [CrossRef]
- Messai, A.; Jouini, F. Micro and macro determinants of non-performing loan. Int. J. Econ. Financ. Issues Econ J. 2013, 3, 852–860. [Google Scholar]
- Naili, M.; Lahrichi, Y. Banks’ credit risk, systematic determinants and specific factors: Recent evidence from emerging markets. Heliyon 2022, 8, e08960. [Google Scholar] [CrossRef] [PubMed]
- Nishimura, K.; Kawamoto, Y. Why does the problem persist? ‘Rational rigidity’ and the plight of Japanese banks. World Econ. 2003, 26, 301–324. [Google Scholar] [CrossRef]
- Quagliarello, M. Banks’ Riskiness over the Business Cycle: A Panel Analysis on Italian intermediaries. Appl. Financ. Econ. 2007, 17, 119–138. [Google Scholar] [CrossRef]
- Syed, A.A.; Aidyngul, Y. Macro economical and bank-specific vulnerabilities of nonperforming loans: A comparative analysis of developed and developing countries. J. Public Aff. 2022, 22, e2414. [Google Scholar] [CrossRef]
- Rachid, S. The determinants of non-performing loans: Do institutions matter? A comparative analysis of the Middle East and North Africa (MENA) and Central and Eastern European (CEE) countries. J. Adv. Stud. Financ. 2019, 10, 96–108. [Google Scholar]
- Shih, V. Dealing with non-performing loans: Political constraints and financial policies in China. China Q. 2004, 180, 922–944. [Google Scholar] [CrossRef]
- Taghizadeh-Hesary, F.; Phoumin, H.; Rasoulinezhad, E. COVID-19 and regional solutions for mitigating the risk of SME finance in selected ASEAN member states. Econ. Anal. Policy 2022, 74, 506–525. [Google Scholar] [CrossRef]
- Touny, M.; Shehab, M. Macroeconomic determinants of non-performing loans: An empirical study of some Arab countries. Am. J. Econ. Bus. Adm. 2015, 7, 11–22. [Google Scholar] [CrossRef] [Green Version]
- Vo, X.V. Bank lending behavior in emerging markets. Financ. Res. Lett. 2018, 27, 129–134. [Google Scholar] [CrossRef]
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Alnabulsi, K.; Kozarević, E.; Hakimi, A. Non-Performing Loans as a Driver of Banking Distress: A Systematic Literature Review. Commodities 2023, 2, 111-130. https://doi.org/10.3390/commodities2020007
Alnabulsi K, Kozarević E, Hakimi A. Non-Performing Loans as a Driver of Banking Distress: A Systematic Literature Review. Commodities. 2023; 2(2):111-130. https://doi.org/10.3390/commodities2020007
Chicago/Turabian StyleAlnabulsi, Khalil, Emira Kozarević, and Abdelaziz Hakimi. 2023. "Non-Performing Loans as a Driver of Banking Distress: A Systematic Literature Review" Commodities 2, no. 2: 111-130. https://doi.org/10.3390/commodities2020007