• Journal of Korea Technology Innovation Society
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• v.12 no.4
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• pp.715-739
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• 2009

Technology innovation activity plays an important role in increasing a sales by bringing on the improvement of product's performance and a profitability by reducing the cost of production. Thus, technology innovation activity can reduce the default risk of firms. However, in spite of these effects of technology innovation activity, this activity can make the default risk of firm because it induce a firm to much investment of resources. This study examines the effect of technology innovation activity on the sales, profitability, and default risk of firms. This study's sample consists of manufacturing firms listed on the Korea Stock Exchange from January 1, 2000 to December 31, 2008. The results show that technology innovation activity has a positive effect on the sales (profitability) but a negative effect on the default risk of firms. Also there is the significant mediation effect of sales and profitability.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.185-192
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• 2022

This study investigates the effect of the semi-supervised learning(SSL) method on predicting default risk of peer-to-peer(P2P) loans. Despite its proven performance, the supervised learning(SL) method requires labeled data, which may require a lot of effort and resources to collect. With the rapid growth of P2P platforms, the number of loans issued annually that have no clear final resolution is continuously increasing leading to abundance in unlabeled data. The research data of P2P loans used in this study were collected on the LendingClub platform. This is why an SSL model is needed to predict the default risk by using not only information from labeled loans(fully paid or defaulted) but also information from unlabeled loans. The results showed that in terms of default risk prediction and despite the use of a small number of labeled data, the SSL method achieved a much better default risk prediction performance than the SL method trained using a much larger set of labeled data.

• Korean Journal of Construction Engineering and Management
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• v.12 no.2
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• pp.121-132
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• 2011

One of the advantages of BOT PF(Project Finance) is the government can be protected from risks involved in projects as the private finances, builds, and operates relevant projects. Moreover, the private may avoid outstanding responsibility in case of default thanks to BOT PF's unique financial structure and characteristics. However, despite increasing attention on risk mitigation effect of financial structure and characteristic of BOT PF to default risk with emerging controversies of capital crunch, introduction of IFRS, and contingent liabilities, valuation of default risk mitigation effect caused by financial structure and characteristics of BOT PF still seems sophisticated due to uncertain cash flows, complexly layered contracts, and their interaction. So, this paper is to show the theoretical frame to assess the default risk mitigation effect of financial structure and characteristic of BOT PF with option pricing and related financial economic theories and to provide some meaningful implications. Finally, this research shows that the financial structure and characteristics of BOT PF help mitigate the default risk and default risk mitigation effect increases as change of relevant variables on financial feasibility gets the BOT project less financially feasible.

• Korea Real Estate Review
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• v.28 no.3
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• pp.21-35
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• 2018

The recent performance of residential mortgages demonstrated how default risk operated separately from prepayment risk. In this study, we investigated the determinants of the borrowers' decisions pertaining to early termination through default from the mortgage performance data released by Freddie Mac, involving securitized mortgage loans from January 2011 to September 2013. We estimated a Cox-type, proportional hazard model with a single risk on fundamental factors associated with default options for individual mortgages. We proposed a mortgage default model that included two specifications of delinquency: one using a delinquency binary variable, while the other using a delinquency probability. We also compared the results obtained from two specifications with respect to goodness-of-fit proposed in the spirit of Vuong (1989) in both overlapping and nested models' cases. We found that a model with our proposed delinquency probability variable showed a statistically significant advantage compared to a benchmark model with delinquency dummy variables. We performed a default prediction power test based on the method proposed in Shumway (2001), and found a much stronger performance from the proposed model.

• Journal of Technology Innovation
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• v.17 no.2
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• pp.55-80
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• 2009

Technology innovation activity plays a pivotal role in constructing the entrance barrier for other firms and making process improvement and new product. and these activities give a profit increase and growth to firms. Thus, technology innovation activity can reduce the default risk of firms. However, technology innovation activity can also increase the firm's default risk because technology innovation activity requires too much investment of the firm's resources and has the uncertainty on success. The purpose of this study is to examine the effect of technology innovation activity on the default risk of firms. This study's sample consists of manufacturing firms listed on the Korea Securities Market and The Kosdaq Market from January 1,2000 to December 31, 2008. This study makes use of R&D intensity as an proxy variable of technology innovation activity. The default probability which proxies the default risk of firms is measured by the Merton's(l974) debt pricing model. The main empirical results are as follows. First, from the empirical results, it is found that technology innovation activity has a negative and significant effect on the default risk of firms independent of the Korea Securities Market and Kosdaq Market. In other words, technology innovation activity reduces the default risk of firms. Second, technology innovation activity reduces the default risk of firms independent of firm size, firm age, and credit score. Third, the results of robust analysis also show that technology innovation activity is the important factor which decreases the default risk of firms. These results imply that a manager must show continuous interest and investment in technology innovation activity of one's firm. And a policymaker also need design an economic policy to promote the technology innovation activity of firms.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.3
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• pp.157-165
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• 2020

This paper estimates the optimum level of reserves in Vietnam based on the approach of reserves' cost-benefit and sovereign risk which is one of developing countries' characteristics. The cost of reserves is the opportunity cost when holding reserves. The benefit of reserves is the loss due to country's default in case that there is no reserves to finance external debt payment. The optimum reserves is found out by minimizing the total of opportunity cost and loss due to country's default with the probability of default. Through the usage of HP Filter method for calculating the loss due to country's default, ARDL regression for the risk premium model and lending rate of VND as proxy for opportunity cost together with the Vietnamese economic data in the period of 2005 - 2017, the empirical results show that the optimum reserves in Vietnam is almost higher than the actual reserves during the research period except the point of Q3/2008 and the last point of research period - Q4/2017. Therefore, Vietnam should continue to increase reserves for safety but Vietnam does not need pushing quickly the speed of increasing reserves. In addition, controlling Vietnamese optimum reserves is necessary to help the actual reserves become reasonable.

• Journal of Soil and Groundwater Environment
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• v.16 no.2
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• pp.12-29
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• 2011

Risk-based screening levels (RBSLs) of some pollutants for residential adults were derived with risk assessment tools developed by United States Environmental Protection Agency (USEPA), American Society for Testing and Materials (ASTM), and Korea Ministry of Environment (KMOE) and compared each other. To make the comparison simple, ingestion of soil, dermal contact with soil, outdoor inhalation of vapors, indoor inhalation of vapors, and inhalation of soil particulates were chosen as exposure pathways. The results showed that the derived RBSLs varied for every exposure pathway. For direct exposure pathways (i.e., ingestion of soil and dermal contact with soil), the derived RBSLs varied mainly due to the different default values for exposure factors and toxicity data. When identical default values for the parameters were used, the same RBSLs could be derived regardless of the assessment tools used. For inhalation of vapors and inhalation of soil particulates, however, different analysis methods for cross-media transfer rates were used and different assumptions were established for each tool, identical RBSLs could not be obtained even if the same default values for exposure factors were used. Especially for inhalation of soil particulates pathway, screening level derived using KMOE approach (most conservative) was approximately 5000~10000 times lower than the screening level derived using ASTM approach (least conservative). Our results suggest that, when deriving RBSL using a specific tool, it is a prerequisite to technically review the analysis methods for cross-media transfer rates as well as to understand how the assessment tool derives the default values for exposure factors.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.2
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• pp.119-131
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• 2024

In recent years, there has been a significant growth in loan comparson services offered by fintech platforms in South Korea. However, it has been reported that loan comparison platform users tend to have a higher risk of default compared to non-users. This paper investigates the difference in platform-specific credit risk factors using survival analysis models - Kaplan-Meier curves and Accelerated Failure Time (AFT) model. Our findings show that, relative to non-users, users of loan comparison platforms are characterized by elevated default rates, a greater propensity for home ownership, lower credit scores, and shorter loan durations. Furthermore, our AFT models elucidate the variance in default risk among the various loan comparison service platforms, highlighting the imperative for customized strategies that address the unique risk profiles of customers on each platform.

• 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.13 no.3
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• pp.685-699
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• 2006

Commercial banks and other related areas have developed internal models to better quantify their financial risks. Since an appropriate credit risk model plays a very important role in the risk management at financial institutions, it needs more accurate model which forecasts the credit losses, and statistical inference on that model is required. In this paper, we propose a new method for estimating a default rate. It is a Bayesian approach using the power prior which allows for incorporating of historical data to estimate the default rate. Inference on current data could be more reliable if there exist similar data based on previous studies. Ibrahim and Chen (2000) utilize these data to characterize the power prior. It allows for incorporating of historical data to estimate the parameters in the models. We demonstrate our methodologies with a real data set regarding SOHO data and also perform a simulation study.