Search results1 – 10 of over 59000
As of 1982, federal credit unions were allowed to add select employee groups and thus create institutions with multiple-group common bonds. We examine the efficiency of…
As of 1982, federal credit unions were allowed to add select employee groups and thus create institutions with multiple-group common bonds. We examine the efficiency of single bond and multiple bond federal-chartered credit unions by using data envelopment analysis (DEA), a non-parametric, linear programming methodology. Results indicate that multiple bond credit unions have better pure technical efficiency than single bond credit unions. However, single bond credit unions appear to be more scale efficient than the multiple bond credit unions. Our results also indicate that members of multiple bond credit unions may derive greater wealth gains than members of single bond credit unions.
The composition of bank liabilities has captured a lot of attention especially after the global financial crisis of 2008–2009. It is argued that a compositional change in…
The composition of bank liabilities has captured a lot of attention especially after the global financial crisis of 2008–2009. It is argued that a compositional change in non-core liabilities reflects the different stages of financial cycle. Banks usually fund their credits with core liabilities, which grow with households’ wealth, but when there is a faster growth in credits compared to deposits, the banks often resort to non-core liabilities to meet the excess demand for loans. This chapter analyses the relationship between non-core liabilities and credits in a small open economy, namely Turkey. It investigates the relationship under alternative settings and presents consistent evidence on a robust relationship between credits and non-core liabilities under all frameworks. The study also verifies that elevated demand for credit may induce some increase in non-core liabilities. Finally, the relationship between non-core liabilities and credit growth is also affirmed in the long run.
This study aims to examine the linear and non-linear effects of corporate social responsibility (CSR) engagement on trade receivables of listed firms in China…
This study aims to examine the linear and non-linear effects of corporate social responsibility (CSR) engagement on trade receivables of listed firms in China. Furthermore, this paper analyzes whether CSR explains the provision for doubtful trade receivables.
The authors use a sample of listed firms in China over the period from 2008 to 2015. System generalized method of moments is used to estimate dynamic panel models.
CSR is positively related to trade receivables, in line with previous studies in this field. Nonetheless, the investigation of the non-linear effect of CSR reveals that CSR has an inverted U-shaped relationship with trade receivables. This implies that at low levels, CSR is more likely to be a tool to mitigate risk and/or build a trusting relationship between suppliers and buyers; whereas, at high levels, CSR is more prone to be subject to agency cost. The authors further find that CSR has a U-shaped relationship with the provision for bad trade receivables, which substantiates the above link between CSR and trade receivables.
Previous studies have extensively examined the link between trade credit extension and firm performance and determinants of trade credit. CSR can be connected to trade receivables in some ways, but very little effort has been exerted in verifying this relationship. In addition, CSR is linearly linked to trade receivables in previous literature, but theoretically, it can be expected to have a non-linear relationship with trade receivables. Furthermore, CSR has not been examined as a determinant of the provision for doubtful trade receivables. The authors aim to void the gaps here by using a sample of listed firms in China.
This study aims to formulate a behavioural credit scoring models for Indian small and medium enterprises (SME) entrepreneurs using certain behavioural and psychological…
This study aims to formulate a behavioural credit scoring models for Indian small and medium enterprises (SME) entrepreneurs using certain behavioural and psychological constructs. Two separate models are built which can predict the credit default and wilful default of the borrowers, respectively. This research was undertaken to understand whether certain psychological and behavioural factors can significantly predict the borrowers’ credit and wilful default.
A questionnaire survey was undertaken by SME entrepreneurs of two Indian states, i.e. Uttar Pradesh and Maharashtra. The questionnaire had two dependent variables: wilful default and credit default and nine independent variables. The questionnaire reliability and validity were ensured through confirmatory factor analysis (CFA) and further a model was built using logistic regression.
The results of this study have shown that certain behavioural and psychological traits of the borrowers can significantly predict borrowers’ default. These variables can be used to predict the overall creditworthiness of SME borrowers.
The findings of this research indicate that using behavioural and psychological constructs, lending institutions can easily evaluate the credit worthiness of those borrowers, who do not have any financial and credit history. This will enhance the capability of financial institutions to evaluate opaque SME borrowers.
There are very few numbers of studies which have considered predicting the credit default using certain psychological variables, but with respect to Asian market, and especially India, there does not exist a single significant study which has tried to fulfil such research gap. Also, this is the first study that has explored whether certain psychological factors can predict the wilful default of the borrowers. This is one of the most significant contributions of this research.
Despite the practice of credit card services by Islamic financial institutions (IFIs) is debatable, Islamic banks (IBs) have been offering this product. Both Muslim and…
Despite the practice of credit card services by Islamic financial institutions (IFIs) is debatable, Islamic banks (IBs) have been offering this product. Both Muslim and non-Muslim customers have subscribed to the products. Thus, it is critical to analyse the strategy of IBs’ moral messages in reminding their Muslim and non-Muslim customers to repay their credit card debts. This paper aims to investigate this issue in Indonesia using data mining via machine learning.
This study examines the IBs’ customers across the 32 provinces of Indonesia regarding their moral status in credit card debt repayment. This work considers 6,979 observations of the variables that affect the moral status of the IBs’ customers in repaying their debt. The five types of data mining via machine learning (i.e. Boruta, logistic regression, Bayesian regression, random forest, XGBoost and spatial cluster) are used. Boruta, random forest and XGBoost are used to select the important features to investigate the moral aspects. Bayesian regression is used to get the odds and opportunity for the transition of each variable and spatially formed based on the information from the logistical intercepts. The best method is selected based on the highest accuracy value to deliver the information on the relationship between moral status categories in the selected 32 provinces in Indonesia.
A different variable on moral status in each province is found. The XGBoost finds an accuracy value of 93.42%, which the three provincial groups have the same information based on the importance of the variables. The strategy of IBs’ moral messages by sending the verse of al-Qur’an and al-Hadith (traditions or sayings of the Prophet Muhammad PBUH) and simple messages reminders do not impact the customers’ repaying their debts. Both Muslim and non-Muslim groups are primarily found in the non-moral group.
This study does not consider socio-economic demographics and culture. This limitation calls future works to consider such factors when conducting a similar topic.
The industry professionals can take benefit from this study to understand the Indonesian customers’ moral status in repaying credit card debt. In addition, future works may advance the recent findings by considering socio-cultural factors to investigate the moral status approach to Islamic credit warnings that is not covered by this study.
This work finds that religious text of credit card repayment reminders sent to Muslims in several provinces of Indonesia does not affect their decision to repay their debts. To some extent, this finding draws a social issue that the local IBs need to consider when implementing the strategy of credit card repayment reminders.
This study credits a novelty in the discourse of data science for Islamic finance practices. Specifically, this study pioneers an example of using data mining to investigate Islamic-moral incentives in credit card debt repayment.