Search results

To enhance the loan repayment performance of microfinance institutions (MFIs) in Pakistan, this study aims to analyze the direct impact of social capital and loan credit terms on loan repayment performance and microenterprises’ business performance while considering the mediating role of microenterprises’ business performance on the relationship between social capital, loan credit terms and loan repayment performance.

The analysis was conducted based on the data gathered via a questionnaire distributed to 316 microenterprises owners. The respondents were selected using the stratified sampling technique by dividing the target population into three influential groups of manufacturing, trading and services microenterprises. The reliability and validity of the constructs were established using (1) factor loading, (2) Cronbach’s alpha, (3) composite reliability, (4) average variance extracted, (5) the variance inflation factor, (6) the Fornell–Larcker criterion and (7) the heterotrait–monotrait ratio. The structural equation modeling technique was then applied, and the hypotheses were tested based on the structure model generated through bootstrapping by using partial least squares structural equation modeling.

The results confirm the direct impact of social capital and loan credit terms on microenterprises’ business performance and loan repayment performance. It also supports the mediating role of microenterprises’ business performance toward the relationship between social capital, loan credit terms and loan repayment performance while considering the direct impact of microenterprises’ business performance on loan repayment performance.

To date, the direct impact of social capital and loan credit terms on microenterprises’ business performance and loan repayment performance has been hardly investigated in the context of Pakistan. This study also examines the mediating role of microenterprises’ business performance toward social capital, loan credit terms and loan repayment performance. The findings will enable both MFIs and microenterprises to improve their business performance and loan repayment performance through enhanced social ties and the development of more flexible credit products that protect the borrowers’ interests and the interest of lenders.

To improve the loan repayment performance (LRP) of microfinance banks (MFBs) in Pakistan, this study aims to look at the direct impact of multiple borrowing (MB) on LRP and client-business performance (CBP), as well as the direct impact of CBP on LRP. The moderating function of pandemic factors in the relationship between MB and CBP, as well as the mediating effect of CBP in the association between MB and LRP, was also investigated in this study.

A questionnaire was used to obtain data from 531 lower-level workers of microfinance institutions (MFIs) for the study. The respondents were chosen using stratified sampling, which divided the target population into four influential groups: lending officers in agriculture, lending officers in businesses, lending officers in gold loans and lending officers in salary loans. In this study, a two-stage structural equation modeling approach was used, including a measurement model (outer model) and a structural model (inner model). The validity and reliability of the questionnaire were investigated using the measurement model (outer model), whereas PLS-SEM bootstrapping was performed to test the hypothesis and find the relationship among different underpinning constructs by using the structural model (inner model).

The outcomes of this study demonstrate that MB has a direct impact on CBP, and that CBP has a direct impact on LRP. MB, on the contrary, had no direct and significant impact on LRP in this study. The idea that CBP mediates the relationship between MB and LRP, as well as the moderating effect of pandemic factors on the relationship between MB and CBP, is supported by this research.

Until now, the influence of MB on LRP via the mediating role of CBP and the moderating role of a pandemic factor in the setting of Pakistani MFBs has received little attention. During the COVID-19 pandemic, this research also aids MFBs in better understanding MB and its impact on LRP. Furthermore, based on the findings of this study, Pakistani MFIs can enhance their LRP by implementing new lending regulations, particularly with reference to MB and the COVID-19 pandemic.

In this study, the authors explore the effects of politically controlled boards on bank loan performance in both state-owned commercial banks (SCBs) and private sector commercial banks (PCBs) in Bangladesh.

The data consist of 409 bank-year observations from 46 sample SCBs and PCBs of Bangladesh for the period 2008–17. The authors apply ordinary least squares pooled regression with year fixed effect for baseline econometric analyses and generalized method of moments regression for robustness tests after addressing the endogeneity issue.

The regression results reveal that the presence of bank “boards controlled by politically affiliated directors” (PA) have significant positive effects on non-performing loans (NPLs). Similarly, the presence of “boards controlled by politically affiliated directors without substantial ownership interests” (PAWOI) show positive association with NPLs. In contrast, the presence of “boards controlled by politically affiliated directors with substantial ownership interests” (PAOI) exhibit an inverse relationship with NPLs. These findings support ‘agency conflict’ arguments and document that both PA and PAWOI are detrimental to bank loan performance in Bangladesh, while PAOI do not have significant effect on increasing NPLs.

This study contributes to the existing bank governance literature by providing evidence from an emerging economy perspective, where politically affiliated directors (PADs) exploit their positions for personal and/or political gain at the cost of other stakeholders by taking advantage of relaxed regulatory oversights and investor protections.

The scope of this research is to investigate performing loan efficiency for fifty European banks during the period 2008–2017.

The study is structured as a two-stage analysis of performing loan efficiency and its driving factors. In the first stage of the proposed methodology “Data Envelopment Analysis” is used to estimate performing loan efficiency for each bank included in the sample. A bootstrap statistical procedure enhances the findings. In the second stage, the impact of other factors on the efficiency scores of loan performance using tobit regression is investigated.

The results are consistent with the findings of the individual banks' financial analyses. According to the findings of DEA implementation, the evaluated banks may enhance their cost efficiency by 39% on average. In addition, the results indicate that loan efficiency performance improves after 2015, coinciding with the business cycle's upward trend. The tobit regression is employed in the second stage to examine the influence of bank-related and macroeconomic factors on banks' loan management efficiency. According to the findings of the tobit regression, three factors, namely the capital adequacy ratio, GDP per capita and managerial inefficiency, have a substantial influence on performing loan efficiency.

This research investigates the effectiveness of European economic policy in protecting the European banking system from the consequences of the sovereign debt crisis in several euro area members. The results highlight the distance of the Eurozone from the level of the ‘optimal currency area’.

The research documented in this paper aims to examine multiple factors related to borrowers' default in peer-to-peer (P2P) lending in the USA. This study is motivated by the hypothesis that both P2P loan characteristics and macroeconomic variables have influence on loan performance. The authors define a set of loan characteristics, borrower characteristics and macroeconomic variables that are significant in determining the probability of default and should be taken into consideration when assessing credit risk.

The research question in this study is to find the significant explanatory variables that are essential in determining the probability of default for LendingClub loans. The empirical study is based on a total number of 1,863,491 loan records issued through LendingClub from 2007 to 2020Q3 and a logistic regression model is developed to predict loan defaults.

The results, in line with prior research, show that a number of borrower and contractual loan characteristics predict loan defaults. The innovation of this study is the introduction of specific macroeconomic indicators. The study indicates that macroeconomic variables assessed alongside loan data can significantly improve the forecasting performance of default model. The general finding demonstrates that higher percentage change in House Price Index, Consumer Sentiment Index and S&P500 Index is associated with a lower probability of delinquency. The empirical results also exhibit significant positive effect of unemployment rate and GDP growth rate on P2P loan default rates.

The results have important implications for investors for whom it is of great importance to know the determinants of borrowers' creditworthiness and loan performance when estimating the investment in a certain P2P loan. In addition, the forecasting performance of the model could be applied by authorities in order to deal with the credit risk in P2P lending and to prevent the effects of increasing defaults on the economy.

This paper fulfills an identified need to shed light on the association between specific macroeconomic indicators and the default risk from P2P lending within an economy, while the majority of the existing literature investigate loan and borrower information to evaluate credit risk of P2P loans and predict the likelihood of default.

The purpose of this study is to investigate the impact of religiosity on banks’ lending behavior during the COVID-19 pandemic in the USA.

This study uses the evidence from the issuance of Paycheck Protection Program (PPP) loans to relate local religiosity to banks’ participation in the PPP loan program and to banks’ loan portfolio performance during the pandemic.

The results of this study show that banks located in more religious counties have a higher level of lending through the PPP, supporting the ethical and moral concerns cultivated by local religious beliefs. In addition, banks’ lending before the pandemic is more prudential in more religious areas, as reflected in lower losses and higher returns at the onset of the crisis, especially in areas where business activities were most disrupted, supporting the stewardship role encouraged by religiosity.

Thanks to the structure of the PPP loans programs, the authors are able to disentangle the conflicting effects of morality and prudence on banks’ behavior.

This research investigated whether corporate social responsibility (CSR) can alleviate the negative effect of non-performing loans (NPLs) on bank performance.

The research employed a sample of European banks over the 2008–2017 period. To resolve endogeneity and heterogeneity problems, the system generalized method of moments (SGMM) model was employed.

First, bank NPLs were negatively and significantly associated with bank performance as measured by the Q-Tobin ratio and the return on assets (ROA). Second, CSR scores exerted a negative and significant effect on the level of NPLs. Finally, the results indicated that bank performance could benefit from the interactional effect of CSR and NPLs.

This study fills the gap in the debate over the mediating role of CSR in the NPLs – bank performance interrelation. In addition, our SGMM analysis yielded more robust and efficient results while resolving endogeneity and heterogeneity problems concerning CSR and bank performance or risk in corporate finance.

CSR practices can play an essential mediating role in the NPLs–bank performance relationship. CSR activities in the European context may reduce the level of NPLs and increase bank performance.

To the best of the authors’ knowledge, studies of the implications of CSR activities on the banking sector are very limited. Indeed, this paper shows that CSR mediates the relationship between CSR practices and NPLs. The results suggest that bank performance could benefit from the interactional effect of CSR and NPLs.

The bank is termed as an imperative part of the marketing economy. The failure or success of an institution relies on the ability of industries to compute the credit risk. The loan eligibility prediction model utilizes analysis method that adapts past and current information of credit user to make prediction. However, precise loan prediction with risk and assessment analysis is a major challenge in loan eligibility prediction.

This aim of the research technique is to present a new method, namely Social Border Collie Optimization (SBCO)-based deep neuro fuzzy network for loan eligibility prediction. In this method, box cox transformation is employed on input loan data to create the data apt for further processing. The transformed data utilize the wrapper-based feature selection to choose suitable features to boost the performance of loan eligibility calculation. Once the features are chosen, the naive Bayes (NB) is adapted for feature fusion. In NB training, the classifier builds probability index table with the help of input data features and groups values. Here, the testing of NB classifier is done using posterior probability ratio considering conditional probability of normalization constant with class evidence. Finally, the loan eligibility prediction is achieved by deep neuro fuzzy network, which is trained with designed SBCO. Here, the SBCO is devised by combining the social ski driver (SSD) algorithm and Border Collie Optimization (BCO) to produce the most precise result.

The analysis is achieved by accuracy, sensitivity and specificity parameter by. The designed method performs with the highest accuracy of 95%, sensitivity and specificity of 95.4 and 97.3%, when compared to the existing methods, such as fuzzy neural network (Fuzzy NN), multiple partial least squares regression model (Multi_PLS), instance-based entropy fuzzy support vector machine (IEFSVM), deep recurrent neural network (Deep RNN), whale social optimization algorithm-based deep RNN (WSOA-based Deep RNN).

This paper devises SBCO-based deep neuro fuzzy network for predicting loan eligibility. Here, the deep neuro fuzzy network is trained with proposed SBCO, which is devised by combining the SSD and BCO to produce most precise result for loan eligibility prediction.

Regulation is critical for sustainable microfinance sector growth. Under this premise, the study aims to examine the different regulatory noncompliance (RNC) practices prevalent in the operations of microfinance institutions (MFIs) at the ground level.

Both the quantitative and qualitative (observations, interviews and focus group discussions) techniques are used to extract the findings.

The study highlights the different RNC practices exercised by the loan officers at the field level in their microfinance loan disbursements.

This study is based on the primary data collected from microfinance clients. The arguments put forth for the RNC practices are extracted from direct personal interviews with the loan officers and the clients. The role of various dilemmas/circumstances of the loan officers and the beneficiaries that implicate the MFIs in RNC is highlighted.

The reason for concern is the rapid decline in loans to small enterprises which is critical to their performance, compared to large businesses following the periods of banking reformations in Nigeria. Thus, the purpose of this paper is to investigate the influence of risk perception on bank lending behaviour to small enterprises. It also investigates the impact of government intervention, consolidation and recapitalization on the relationship between risk perception and bank lending behaviour to small enterprise.

This study empirically analysed (ordinary least square) secondary data obtained from the Central Bank of Nigeria Statistical Bulletins, Annual Statement of Accounts covering the period 1992–2020.

The results show that the absence of government interventions and the presence of banking reformations have statistically negative significant effect on bank lending to small enterprises. The findings challenge the argument that generally assumes risk aversion of banks towards small enterprise lending because of small enterprise’s inability to prove their credit worthiness and consequently constraining access to finance to the sector. Instead, the results and analysis from this study found theoretical support for the variation of bank behaviour in lending to small enterprises depending on the status of wealth of the financial system.

A key lesson from this study for government concerned about promoting performance of the small enterprise sector is that regulating and enforcing lending requirements on access to debt financing of the sector is necessary if constraints in access debt finance is to be eliminated. Second, while strategies such as bank consolidation, recapitalization may help strengthen and make financially robust the banking system; it places the banks in a gain position where losses looms to them than gain.

This study challenges the argument that generally assumes risk aversion of banks towards small enterprise lending as a result of inability to prove their credit worthiness and consequently constraining access to finance to the sector. Instead, the results and analysis from this study reveal a variation in lending to small enterprises and suggests that the position of the bank in relation to a reference point influences how risk is perceived by the bank and thus impacts on their risk decision-making behaviour.