• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.6
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• pp.3564-3570
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• 2014

This study assessed the validity of establishing and implementing a financial supervisory policy considering that Mutual finance's increase in deposits received raises the rate of loans in the process that the recent financial supervisory authorities applied various regulatory measures to mutual financial organizations. As a result of the analysis with a least squares regression model for the correlation between the rate of increase in the deposits received by Nonghyup Mutual Finance and the default rate of loans through the complete enumeration on the 1,161 the regional Nonghyup branches nationwide from 2005 to 2011, showed that there was no (+) correlation between them the financial supervisory authorities premised but a (-) relation. As Nonghyup is a mutual financial organization with the phenomenon that the application plan of increased deposits received is becoming diversified, the increase in deposits received helps reduce the procurement interest rate of funds, which provides a chance to apply the low interest of loans again, so they have positive effects on the overall loans. Financial Supervisory authorities should re-establish a direction of policy understanding the characteristics of Nonghyup's fund use and the detailed correlation between the rate of increase in deposits received and the default rate.

• KDI Journal of Economic Policy
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• v.44 no.3
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• pp.1-26
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• 2022

This paper analyzes the effects of the cut in the legal maximum interest rate (from 27.4% to 24%) that occurred in February of 2018 on loan interest rates, the default rates, and the loan approval rate of borrowers in the non-banking sector. We use the difference-in-difference identification strategy to estimate the effect of the cut in the legal maximum interest rate using micro-level data from a major credit-rating company. The legal maximum rate cut significantly lowers the loan interest rate and default rate of low-credit borrowers (i.e., high-credit-risk borrowers) in the non-banking sector. However, this effect is limited to borrowers who have not been excluded from the market despite the legal maximum interest rate cut. The loan approval rate of low-credit borrowers decreased significantly after the legal maximum interest rate cut. Meanwhile, the loan approval rate of high-credit and medium-credit (i.e., low credit risk and medium credit risk) borrowers increased. This implies that financial institutions in the non-banking sector should reduce the loan supply to low-credit borrowers who are no longer profitable while increasing the loan supply to high- and medium-credit borrowers.

• The Journal of Industrial Distribution & Business
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• v.9 no.5
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• pp.37-46
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• 2018

Purpose - This paper explores the determinants of bank lending to firms during and after the global financial crisis using firm- and bank-level data to answer the questions what caused the contraction of lending to firms despite the loosening monetary policy during this crisis period. Research design, data, and methodology - We investigate the effects of the monetary policy that followed the global financial crisis on firms borrowing. We use a dynamic panel model to address how firms lending respond to monetary policy. The data are obtained from CRETOP and we consider the manufacturing sector for the analysis to control for unobserved heterogeneity such as industry-specific shocks. Results - The findings from the empirical analysis suggest that both bank- and firm-level characteristics are significant determinants of bank lending. Especially, we find that corporate risk, measured by default risk, is one of the key factors that led to a decline in lending during the crisis. Conclusions - This paper shows that companies borrow more from liquid banks, and high bank capital can also contribute to an increase in a firm's borrowing from banks. Especially, the results confirm that the default rate measured at the firm level has increased during and after the global financial crisis, which implies that default risk interplays with other firm and bank-level characteristics.

• Communications for Statistical Applications and Methods
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• v.19 no.4
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• pp.629-638
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• 2012

We propose some methods to obtain optimal thresholds and classification functions by using various cutoff criterion based on the bivariate ROC curve that represents bivariate cumulative distribution functions. The false positive rate and false negative rate are calculated with these classification functions for bivariate normal distributions.

• The Journal of Society for e-Business Studies
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• v.23 no.3
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• pp.207-224
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• 2018

In this article, an empirical study was conducted by using public dataset from Lending Club Corporation, the largest online peer-to-peer (P2P) lending in the world. We explore significant predictor variables related to P2P lending default that housing situation, length of employment, average current balance, debt-to-income ratio, loan amount, loan purpose, interest rate, public records, number of finance trades, total credit/credit limit, number of delinquent accounts, number of mortgage accounts, and number of bank card accounts are significant factors to loan funded successful on Lending Club platform. We developed online P2P lending default prediction models using discriminant analysis, logistic regression, neural networks, and decision trees (i.e., CART and C5.0) in order to predict P2P loan default. To verify the feasibility and effectiveness of P2P lending default prediction models, borrower loan data and credit data used in this study. Empirical results indicated that neural networks outperforms other classifiers such as discriminant analysis, logistic regression, CART, and C5.0. Neural networks always outperforms other classifiers in P2P loan default prediction.

• Nuclear Engineering and Technology
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• v.31 no.5
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• pp.465-475
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• 1999

The objective of the present work is to assess the analysis capability of two wall film condensation models, the default and the alternative models, of RELAP5/MOD3.2 on condensation experiments in the presence of noncondensable gas in a vertical tube of PCCS of CP-1300. In the calculation of a base case the default model of RELAP5/MOD3.2 under-predicts the heat transfer coefficients, and Its alternative model over-predicts them throughout the condensing tube, Also, both models over-predict the void fractions. The nodalization study shows that the variation of the node number does not change both modeling results of RELAP5/MOD3.2 Sensitivity study for varying input parameters shows that the inlet steam-air mixture flow rate, the inlet air mass fraction, and the inlet saturated steam temperature give significant changes of their heat transfer coefficients Run statistics show that the grind time of the default model is always higher than that of the alternative model by about 23%.

• The Korean Journal of Financial Management
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• v.22 no.2
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• pp.213-226
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• 2005

Adverse selection is a heavily scrutinized subject within the financial intermediary industry. Consensus is reached regarding its effect on the loan interest rate. Despite the similar features of financial service offered by the credit card, we still have controversy regarding credit card interest rate on how is adverse selection incurred with the change of interest rate. Thus, this paper explores how does the adverse selection, if ever, take place and affect the credit card interest rate. Information asymmetry regarding the credit card users' type represented by the default probability is assumed. The users are assumed to be rational in that they want to minimize the per unit dollar expense associated with the commercial transaction and financing between the two typical payment methods, cash and credit card. Suppliers, i.e. credit card companies, would like to maximize their profit and would be better off with more pervasive use of credit cards over the cash. Then we could show that the increasing credit card interest rate is subject to the adverse selection, sharing the same tenet with that of the bank loan interest rate proposed by Stiglitz and Weiss. Hence the current theory predicts that credit card market also suffers from adverse selection with increasing interest rate.

• The Korean Journal of Applied Statistics
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• v.24 no.2
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• pp.253-268
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• 2011

In this paper, we estimate systematic risk from credit migration (or transition) matrices under "Asymptotic Single Risk Factor" model. We analyzed transition matrices issued by KR(Korea Ratings) and concluded that systematic risk implied on credit migration somewhat coincide with the real economic cycle. Especially, we found that systematic risk implied on credit migration is better than that implied on the default rate. We also emphasize how to conduct a stress test using systematic risk extracted from transition migration. We argue that the proposed method in this paper is better than the usual method that is only considered for the conditional probability of default(PD). We found that the expected loss critically increased when we explicitly consider the change of credit quality in a given portfolio, compared to the method considering only PD.

• Communications for Statistical Applications and Methods
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• v.19 no.2
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• pp.277-286
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• 2012

For credit assessment models, the ROC curves evaluate the classification performance using two univariate cumulative distribution functions of the false positive rate and true positive rate. In this paper, it is extended to two bivariate normal distribution functions of default and non-default borrowers; in addition, the bivariate ROC curves are proposed to represent the joint cumulative distribution functions by making use of the linear function that passes though the mean vectors of two score random variables. We explore the classification performance based on these ROC curves obtained from various bivariate normal distributions, and analyze with the corresponding AUROC. The optimal threshold could be derived from the bivariate ROC curve using many well known classification criteria and it is possible to establish an optimal cut-off criteria of bivariate mixture distribution functions.

• Journal of the Korean Data and Information Science Society
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• v.22 no.2
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• pp.149-157
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• 2011

Undecided inference could be regarded as a missing data problem such as MARand MNAR. Under the assumption of MAR, undecided inference make use of logistic regression model. The probability of default for the undecided group is obtained with regression coefficient vectors for the decided group and compare with the probability of default for the decided group. And under the assumption of MNAR, undecide dinference make use of logistic regression model with additional feature random vector. Simulation results based on two kinds of real data are obtained and compared. It is found that the misclassification rates are not much different from the rate of rawdata under the assumption of MAR. However the misclassification rates under the assumption of MNAR are less than those under the assumption of MAR, and as the ratio of the undecided group is increasing, the misclassification rates is decreasing.