Rate Of Non-performing Loans Research Articles

Systemically important financial institutions in China: from view of tail risk spillover network

ABSTRACT The investigation of the systemically important financial institutions (SIFIs) plays a key role in coping with systemic risk. We first adopt the GARCH-Copula-CoVaR model to establish tail risk spillover networks of China’s financial institutions. We then employ the systemic risk emitters (SRE) and receivers (SRR) to measure the SIFIs. Finally, we utilize the panel data regression model to analyse the determinants of the rank of SRE and SRR. We find that state-owned banks and large insurance companies show systemic importance, while some commercial banks are also SIFIs due to their high value of SRR. Furthermore, the growth rate of total assets, leverage, nonperforming loans, price-earnings ratio and firm size are the common factors that affect the SIFIs.

Fintech platforms: Lax or careful borrowers\u2019 screening?

Can peer-to-peer lending platforms mitigate fraudulent behaviors? Or have lending players been acting similar to free-riders? This paper constructs a new proxy to investigate lending platform misconduct and compares the FICO score and the LendingClub credit grade. To examine whether the lack of verification by the Fintech platform affects lenders’ collection performance, I explore the recovery rate (RR) of non-performing loans through a mixed-continuous model. The regression results show that the degree of prudence taken by the lending platform in the pre-screening activity negatively affects the detection of some misreporting borrowers. I also find that the Fintech platform’s missing verification information (e.g., annual income and employment length) affects the RR of non-performing loans, thereby hampering lenders’ collection performance.

Determinants of Bank Profitability in CEE Countries: Evidence from GMM Panel Data Estimates

Given the high resilience of the Central and Eastern Europe (CEE) banking sectors during the last financial crisis and their major role in the CEE region in financing the economy and supporting the high growth rates achieved there, our paper investigates the determinants of banking profitability in the CEE banking sectors based on a Generalized Method of Methods (GMM) approach using data between 2009 and 2018. We have selected determinants from the macroeconomic factors and from the financial-banking specific factors using a two-step GMM method. Our findings demonstrate that unemployment rate, inflation, budget balance, non-governmental credit, non-performing loan rates, concentration rate and capitalization rate negatively impact on the banking profitability in the CEE banking sectors. According to these findings, some policy recommendations were elaborated.

Analysis of the Effect of Bank Soundness and Macroeconomics on Financial Distress in Conventional Commercial Banks

This research aims to analyze the effect of bank soundness and macroeconomics on the financial distress of conventional banking in Indonesia in the 2015-2019 period. The bank soundness is measured using the Risk-Based Bank Rating or RGEC method which is proxied by the ratio of non-performing loan (NPL), size of board directors, return on asset (ROA), capital adequacy ratio (CAR). The macroeconomic variable was measured using exchange rate while the dependent variable was measured by Altman Z-Score modification. The population of this research is all conventional banking companies listed on the Indonesian Stock Exchange in 2015-2019. The method used for selecting the sample is purposive sampling. The sample obtained was 37 banks with a five years observation period. This research is using multiple linear regression analysis. The result of this research indicates that the size of the board direction, return on asset, and capital adequacy ratio harms the financial distress of conventional banking in Indonesia in the 2015-2019 period. While the non-performing loan and exchange rate do not affect the financial distress of conventional banking in Indonesia in the 2015-2019 period.

Investigating the Factors Influencing Commercial Bank Lending in Nigeria: A Consolidation and Interaction Effect

This study provides empirical evidence on what determines bank lending in Nigeria by considering the pre and post consolidation effect and interaction effect using annual data from 1990-2019. This study employs multiple regression analysis. The regression model examines the effect of total savings (TOS), non- performing loan rate (NPLR) and number of bank branches (NBB) on credit to private sector (CPS). The study also investigates the interaction effect of total savings and Number of bank branches in explaining the combined factors that influences Nigerian Commercial Banks’ lending. The regression results reveal that before and after structural break, none of the variables is significant to determine commercial bank lending in Nigeria. It also reveals that prior to consolidation only TOS and NPLR had positive effect on CPS while all the variables had negative effects on CPS after consolidation period. From the result of the interaction effect, it’s evident that the direct effect of total savings, Number of bank Branches and Non-performing loan on credit to private sectors is positive while only total savings and Nonperforming loan exact significant effects. The interaction effect of total savings and Number of bank branches shows a negative but significant relationship with credit to private sector. This study therefore concludes that there exists an interaction effect in the model and that none of the variables is significant in determining commercial bank lending in Nigeria before and after consolidation. However, the multiplicative effect of total savings and number of branches on commercial bank lending behavior is significant. Therefore, the study recommends that total savings, non-performing loans and Number of bank branches should be jointly managed and utilized to control bank lending behavior in Nigeria.

Credit Reference Bureau Policy Effect on Loan Performance of Commercial Banks in Kenya

One of the major challenges facing banks all over the world is the increasing rate of non-performing loans. Government and the banks themselves have devised various mechanisms to reduce non-performing loans while increasing credit uptake in the credit market. However, the scenario happens to be opposite; as demand for credit increases, so are the non-performing loans. In Kenya, the government introduced Credit Reference Bureaus in 2009 in order to address this problem. However, the problems continue haunting the banks raising questions on whether credit information sharing is working or not. The purpose of this research project was to establish the effect of credit information sharing policyon loan performance of commercial banks in Kenya. The study employed mixed method research design involving secondary methods. The target population was the 42 commercial banks in Kenya and used 210 credit officers. Collection of data was through the questionnaires which were administered to the bank credit officers using drop and pick approach. Document analysis was used to obtain credit records of the banks. The findings shown that non-performing loans increased significantly even after implementation of the credit information sharing (CRB) policy, an implication that the main expected outcome was not achieved. Though there was a significant increase in profits after implementation of the CRBs, a close analysis of the trend shows that there was no major shift that could be attributed to the policy. Responses from the accounting officers also proved that through the CRB policy was well intended, the goals have not been met largely due to lack clarity on the criteria for listing and delisting defaulters. The study therefore recommends the commercial banks and the CRBs should make their policies on listing and delisting defaulters clear and easy to implement.

The Impact of Macroeconomic, Social and Governance Factors on the Sustainability and Well-Being of the Economic Environment and the Robustness of the Banking System

The paper highlights the connection between the European Union banking system and a set of representative factors—macroeconomic, social, and governance factors—selected from the perspective of sustainability and well-being. The analysis is carried out as a panel regression on EU member countries with annual data for 2005–2018, and it explores the impact of the selected factors on the robustness of the banking systems in the European Union countries. The analyzed variables to reflect the robustness of the banking system were the domestic credit to the private sector and the nonperforming loans (NPL) rate. Those indicators are of high relevance and concern within the current pandemic context. The results show that the banking development degree influences the increase of private-sector lending and the decrease of the NPL rate. Social and governance factors impact differently the level of private sector and NPL rate. All macroeconomic indicators used to influence the level of private-sector lending. The research reflects the fact that to promote and adopt a culture of sustainability and to ensure well-being, a close collaboration between all sectors of an economy is needed, together with a strong policy interconnection and harmonization between micro and macro.

Analysis of Funding Strategy, Credit Performance, and Banking Profitability. (Case Study of CIMB-NIAGA Bank in Indonesia)

This study is to analysis funding strategy and credit performance, and its effect on Banking Profitability. The indicator of funding strategy measured with the ratio of time deposits with a total of third-party funds (DTPF) or a third-party fund structure (TPFS), credit performance measured by credit growth (CG) and non-performing loans rate (NPL), while banking profitability with return on assets (ROA).The descriptive and verification methods with the quantitative approach will be used in this research. Secondary data sources are from the quarterly published financial statements of the CIMB Niaga Bank in Indonesia of 2012 – June 2020 period, and multiple regression used for data analysis. The study results found TPFS partially has a negative but not significant effect on ROA. CG partially has a positive but not significant effect on ROA while NPL partially has a negative and significant effect on return on assets. Simultaneously TPFS, CG and NPL have a significant effect on ROA. The ROA is influenced by TPFS, CG, and NPL of 71.2% while the remaining 28.8% is influenced by other factors.

The debtor-attributes information and the probability of performing loans in the microfinance sector

Based on the recent evidence of the high rate of non-performing loans in Indonesia’s microfinance sector, there is a need to formulate a mechanism that could control such credit risk. This study attempts to identify the pivotal determinant factorsresponsible for performing loans. In particular, this research proposes demand-side factors, such as debtor-attributes information, as critical for ensuring installment payments. Using unique data from microfinance in Central Java, it employs logistic regression analysis to prove that debtors’ age and type of collateral significantly affect loan performance. This paper further shows that other variables, such as the payment period and the interest rate, also substantially affect credit risk. These findings have empirical and practical significance. For practical purposes, this research develops the new perspective that debtor-specific information, particularly on their behavioral aspects, requires more in-depth review. This insight may be useful for developing new credit-analyzing tools to alleviate severe non-performing loans. Empirically, this research improves the study of antecedent variables that influence performing loans.

Credit Risk and (Influencing Factors of) Non-Performing Loans

The main purpose of each banking institution is to operate profitably in order to maintain a stable and sustainable growth. However, the existence of high levels of non-performing loans (NPLs) in the banking industry negatively affects the level of private investment and limits the scope of bank credit to borrowers. It is a common consensus that the aggregate level of non-performing loans is associated with bank failure and financial erosion in both developing and developed countries. The dependent variable under investigation was the non-performing loans rate (NPL) used in a panel data model for 78 countries for the period 2007-2015. Banking performance as well as economic growth negatively and statistically significantly influence the rate of non-performing loans while the degree of accessibility to banking products and services is positively associated with the NPL rate. Another conclusion of the study would be that the degree of concentration of the banking market is not statistically significant when associated with the existence of non-performing loans.

Measuring the Effect of Covid-19 on Bank Lending: Empirical Evidence from Albania

This study aims to empirically contribute to the identification and evaluation of microeconomic and macroeconomic indicators at the level of lending in Albania. It identifies a number of important factors, such as the level of gross domestic product, return on assets, unemployment rate, inflation rate, non-performing loans rate, capital adequacy, liabilities and regulatory capital to assets risk weighted. Quantitative analysis and econometric models will study the quantitative impact of each of these factors on both the level of net credit stock and the level of new credit. The creation of these 2 econometric models will serve us to measure and evaluate the changes encountered in the dependent variable over a given period of time, as a result of shocks from other variables. Also, a current and important contribution to this thesis relates to the impact assessment of COVID-19. In order to maintain the simplicity and usefulness of the model, some realistic features of the current economy have been left out, such as the level of loan repayment etc. The study period is from the first quarter of 2009 to the fourth quarter of 2020. The data used were obtained from the Bank of Albania and the Albanian Association of Banks, which were presented in the form of a time series. Despite the limited number of data considered regarding the impact of COVID-19 as well as their temporal distribution, this study with the work it performs, serves as a good starting point for further studies in this field.

The Influence of Digital Finance Development on the Operating Performance of China's Commercial Banks

Taking China's listed commercial Banks as the object of study, using their financial datas from 2014 to 2018, selecting net loan deals and third-party payment transaction scale growth as independent variables, which measure digital level of financial development. profitability and growth rate of non-performing loans to assets as dependent variables, which measure commercial banks operating performance characterization. by establishing the panel data model. To empirically test the impact of digital finance development on the operating performance of China's commercial banks. The empirical results show that the development of digital finance can not only improve the profit margin of commercial banks' assets, but also help reduce their non-performing loan ratio to improve the performance of commercial banks.

The impact of non-performing loans on bank lending in Europe: An empirical analysis

In the aftermath of the global financial crisis, considerable attention has been paid to the accumulation of non-performing loans in the balance sheet of European banks and to its potential negative effects on bank lending to the real economy. Using a dataset composed of bank-specific information and country aggregates, we study the impact of the stock and the flow of non-performing loans on the lending activities of a sample of 75 European banks between 2014 and 2018. In general, higher rates of non-performing loans, together with other variables, are associated with lower growth rates of performing loans. This effect persists across several econometric specifications and is more significant for those banks exhibiting lower growth rates of performing loans. Similarly, our econometric analysis suggests that banks with higher decreases in their rate of non-performing loans tend to lend more to the real economy, an effect which is particularly intense at the right tail of the distribution. The findings of our paper can be useful for policymakers when addressing the resolution of non-performing loans in banks.

Macroeconomic Determinants of Nonperforming Loans of Romanian Banks

The banking sector plays an important role in the development of any economy. The performance of the loans in bank portfolios is a critical issue for the banking sector. The increased number of nonperforming loans (NPLs) after the financial crisis of 2008 has questioned the robustness of many banks and the stability of the entire sector. Our study aims to present the most important aspects related to NPLs and to investigate some macroeconomic determinant factors affecting the rate of NPLs in Romania. Based on a set of data for the period 2009–2019, the analysis of NPLs was made using linear regression. The results showed that all selected independent variables (exchange rates of the most used currencies (EUR, USD and CHF), unemployment rate, and inflation rate) have a significant impact on the dependent variable NPL. The study reveals strong correlations between NPLs and the macroeconomic factors studied and that the Romanian economy is clearly connected to the quality of the loan portfolios. Additionally, an econometric analysis of the empirical causes of NPLs shows that the RON–CHF exchange rate has been the main factor in increasing the NPL ratio in the last 5 years in Romania.

Determinants of Credit Risk in the Banking Sector of Ghana: A Panel Co-integration Approach

The economic development of any nation hinges on the health of its financial system. In recent years, the health of the Ghanaian Banking sector has been affected severely as a result of high levels of non-performing loans (NPLs), which has been identified as a major threat to the overall profitability and survival of banks. To minimize the impact of NPLs on the financial sector, key stakeholders such as the government, bank officials and regulators are working hard in that regard. However, any policy response aimed at dealing with the high rate of non-performing loans first requires the understanding of the underlying determinants of NPLs. Against this backdrop, this paper apply panel co-integration techniques to investigate the determinants of credit risk (NPLs) in the banking sector of Ghana. We use NPL as a proxy to measure credit risk and assess how it is influenced by macroeconomic and bank-specific factors. A balanced panel data of 16 universal banks in Ghana from 2010 to 2016 has been analyzed using Panel co-integration techniques such as Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS). Our result shows that growth in the economy, measured by Gross Domestic Product (GDP) has significant influence on the NPLs of banks in the long-run. The results further revealed that capital adequacy, profitability and liquidity of banks are significant predictors of NPLs. However, our results suggest that bank size, inflation and interest rate have statistically insignificant influence on the NPLs of Ghanaian banks. The study recommend, among others, that whereas it is important for government and policymakers to work to improve macroeconomic outcomes, banks should also improve their capital adequacy, profitability, and efficiency position as these bank-specific interventions could significantly improve credit quality and minimize NPLs.

LEGAL PROTECTION FOR DEBTORS OF ONLINE LOANS

Business concepts has experienced change from conventional (offline) to that which is digital (online) to win the market. This includes businesses in the financial technology (FinTech). This research aims to analyze the quality of loans in Indonesia FinTech-Peer to Peer (P2P) Lending. The objects of this research are seventy-two companies which establish FinTech-P2P lending. The tool used in this research is the analysis of the loan quality and the data trend from January until October 2018. Results of this research show that borrower and lender accounts have increased by 10,8 times, and it has increased by 1,8 times compared to the previous year. P2P lending has distributed loans as much as Rp.15.990.143.141.355 until October 2018. This number has grown 5,3 times since January of that year. Most of the loans in the P2P lending are current loans, in which its monthly payment is less than 30 days past due as of any date of determination. On average, the loan quality in the P2P lending is dominated by current loans, which reach 97,09%. The rest are non-current loans (1,90%) and bad loans (1,03%) every month. The low rate of non-performing loans shows a good quality of financial transaction. The information from this research may be used as a consideration for the society in using FinTech-P2P Lending for lending funds and investment.

Bank business models as a driver of cross-border activities

Much of the post-crisis moderation in international banking has been shown to be attributable to euro area banks. Using a confidential bank-level dataset on euro area resident banks, this paper sheds light on the type of banks responsible for the decline in cross-border lending. Overall, globally systemically important banks (G-SIBs) were markedly different from other bank types resident in the euro area, as they not only increased their extra-euro area positions, but also contributed to maintaining cross-border flows within the euro area over the past decade. In addition, we also highlight that banks with initially higher capitalisation, more deposit funding and lower non-performing loan rates exhibit less cross-border retrenchment. These findings have clear policy implications for the design of bank regulations appropriate for a deepening European Banking Union.

Risk Assessment of Internet Credit Based on Big Data Analysis

In recent years, network technology has continued to develop, and Internet finance has rapidly developed into a new business area. Internet credit is one of the important ways for banks to conduct business, and the scale of online credit has continued to expand. Due to the existence of various unpredictable factors, frequent emergencies, and online financial fraud, the overall market risk in the field of online credit has increased, and the rate of non-performing loans has continued to increase. Online financial fraud cases show that online credit risk has become one of the most prominent risks in the operation of commercial banks, which has a direct impact on the stability and development of commercial banks. We can build a bank database system based on big data, introduce professional big data analysis technical personnel, and constantly improve the big data sharing analysis platform, so that commercial banks can use system data more fully and effectively, and facilitate relevant business personnel to use big data technology for analysis and calculation. Big data is constantly produced, which provides basic materials for online credit risk assessment. Big data analysis technology is gradually mature, and it has the necessary conditions for online credit risk assessment. Based on the theories and technologies related to big data analysis, this paper comprehensively evaluates the online credit risk in the form of example data analysis, thereby effectively reducing the online credit risk coefficient.

Country output and financial black holes: public–private partnerships and the Laffer curve, fiscal corruption risk, and bailout rate of non-performing loans

This study develops the link between output and fiscal corruption risk in public–private partnership (PPP) schemes and the government bailout rate of non-performing loans (NPLs). The model assumes that corruption is widespread in such public investment programs. The objective functions of the government and PPP firms include fiscal corruption risk, given that the PPP firm and tax inspector can ‘effectively’ negotiate bribes. The model solves for the optimal country output (i.e., aggregate productivity) according to the Lagrange method. Long-term prospects are introduced to solve the problem with commercial banks, as most PPPs borrow from commercial banks. The results reaffirm that tax policy can exacerbate the country’s output loss. Although the equilibria between aggregate productivity and the Laffer curve lack a direct link to fiscal corruption risk, their magnitude does and depends on the number of PPPs. The PPP transfer from the government in period 2 and the number of PPPs rather than government expenditure in period 1 and the Laffer curve (tax revenues) mainly determines the bailout rate of PPPs’ NPLs. The article concludes with suggestions to prevent tax evasion and fiscal corruption risk in PPP schemes by using a cluster of cooperation, and recommends further research into cultural aspects.

Determination of influential financial factors on non-performing loans: evidence from Turkey

Turkish banking sector (TBS) has been faced with a growth of the rate of non-performing loans (NPL) over recent years. The level of NPL plays a restricting role of credit providing capacity of banks. So, necessary financing for economic growth cannot be provided and hence a variety of uncertainties and deteriorations in macro economy have been seen in Turkey. In this context, determination of factors affecting of NPL increasing is required to keep under control. Therefore, this study presents the critical factors on NPL by using 14 variables, quarterly data for the period of 2003Q1-2019Q2, and the multivariate adaptive regression splines (MARS) method. The results provide the evidence that the commercial credit interest rates, provisions, risk-weighted assets (RWA), credits, legal equity, net profits, and EUR/TL are the most influential factors on NPL, respectively.