• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.511-517
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• 2021

In this paper, we analyzed the impact of capital adequacy requirements on the profitability of Korean banks using DOLS model. As a result of the analysis, the impact of BIS capital ratios on commercial and regional banks was different. Demand for capital adequacy has a greater and more significant negative impact on regional banks than on commercial banks. It was shown that bank characteristic variables rather than macroeconomic variables have a more significant effect on bank profitability. In addition, a rise in the BIS capital ratio reduces the profitability of commercial and regional banks, and the higher the ratio of loan-loss provisions, the stronger the relationship. In the case of commercial banks, it is estimated that the demand for capital adequacy did not have a significant impact as they are relatively large and faithful in capital compared to regional banks. However, in the case of regional banks, safer assets need to be selected to meet the BIS capital ratio, and the increasing propotion of these safe assets seems to have a relatively greater negative impact on profitability. Consequency, the financial authorities should consider this results and implement the bank's capital regulation policy.

• Journal of Preventive Medicine and Public Health
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• v.55 no.5
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• pp.455-463
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• 2022

Objectives: Economic hardship has a serious impact on adolescents' mental health. The financial impact of the coronavirus disease 2019 (COVID-19) pandemic was more severe for low-income families, and this also impacted adolescents. This study aimed to examine the associations of economic deterioration (ED) caused by the COVID-19 pandemic and low socioeconomic status (SES) with adolescents' suicidal behaviors. Methods: This study analyzed data from the 2020 Korea Youth Risk Behavior Web-based Survey, which included 54 948 middle and high school students. Odds ratios (ORs) of suicidal ideation, suicidal planning, and suicide attempts related to ED and SES were calculated using multivariable logistic regression. We calculated relative excess risks due to interaction to assess additive interactions. Results: The ORs for suicidal ideation, suicidal planning, and suicide attempts related to combined severe ED and low SES were 3.64 (95% confidence interval [CI], 3.13 to 4.23), 3.88 (95% CI, 3.09 to 4.88), and 4.27 (95% CI, 3.21 to 5.69), respectively. Conclusions: ED and low SES were significantly associated with suicidal behaviors in adolescents. Although no significant additive interaction was found, the ORs related to suicidal ideation, suicidal planning, and suicide attempts were highest among adolescents from low-income families with severe ED. Special attention is needed for this group, considering the increased impact of economic inequality due to the COVID-19 pandemic.

• Safety and Health at Work
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• v.14 no.3
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• pp.272-278
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• 2023

Background: Some working conditions may pose a higher physical or psychological demand to pregnant women leading to increased risks of pregnancy complications. Objectives: We assessed the association of woman's employment status and the industrial classification with obstetric complications. Methods: We conducted a national population study using the National Health Information Service database of Republic of Korea. Our analysis encompassed 1,316,310 women who experienced first-order live births in 2010-2019. We collected data on the employment status and the industrial classification of women, as well as their diagnoses of preeclampsia (PE) and gestational diabetes mellitus (GDM) classified as A1 (well controlled by diet) or A2 (requiring medication). We calculated odds ratios (aORs) of complications per employment, and each industrial classification was adjusted for individual risk factors. Results: Most (64.7%) were in employment during pregnancy. Manufacturing (16.4%) and the health and social (16.2%) work represented the most prevalent industries. The health and social work exhibited a higher risk of PE (aOR = 1.11, 95% confidence interval [CI]: 1.03-1.21), while the manufacturing industry demonstrated a higher risk of class A2 GDM (1.20, 95% CI: 1.03-1.41) than financial intermediation. When analyzing both classes of GDM, women who worked in public administration and defense/social security showed higher risk of class A1 GDM (1.04, 95% CI: 1.01, 1.07). When comparing high-risk industries with nonemployment, the health and social work showed a comparable risk of PE (1.02, 95% CI: 0.97, 1.07). Conclusion: Employment was associated with overall lower risks of obstetric complications. Health and social service work can counteract the healthy worker effect in relation to PE. This highlights the importance of further elucidating specific occupational risk factors within the high-risk industries.

• Journal of Preventive Medicine and Public Health
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• v.57 no.1
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• pp.18-27
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• 2024

Objectives: This study investigated the association between exacerbated economic hardship during the coronavirus disease 2019 (COVID-19) pandemic and changes in the health behaviors of Korean adolescents. Methods: We analyzed data from the 2021 Korea Youth Risk Behavior Survey and included 44 908 students (22 823 boys and 22 085 girls) as study subjects. The dependent variables included changes in health behaviors (breakfast habits, physical activity, and alcohol use) that occurred during the COVID-19 pandemic. The aggravation of economic hardship by COVID-19 and the subjective economic status of the family were used as exposure variables. Multiple logistic regression analysis was utilized to calculate the prevalence odds ratios (PORs). Results: Severe exacerbation of a family's economic hardship due to COVID-19 was negatively associated with the health behaviors of adolescents, including increased breakfast skipping (POR, 1.85; 95% confidence interval [CI], 1.55 to 2.21 for boys and POR, 1.56; 95% CI, 1.27 to 1.92 for girls) and decreased physical activity (POR, 1.37; 95% CI, 1.19 to 1.57 for boys and POR, 1.38; 95% CI, 1.19 to 1.60 for girls). These negative changes in health behaviors were further amplified when combined with a low subjective family economic status. Conclusions: The experience of worsening household hardship can lead to negative changes in health behavior among adolescents. It is crucial to implement measures that address the economic challenges that arise from stressful events such as COVID-19 and to strive to improve the lifestyles of adolescents under such circumstances.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• Information Systems Review
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• v.16 no.3
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• pp.161-177
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• 2014

Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.

• KDI Journal of Economic Policy
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• v.32 no.4
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• pp.1-34
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• 2010

We conduct a comprehensive risk analysis of household debt in Korea for the first time using the whole sample credit bureau (CB) data of 2.2 million individual debtors. After analysing debt service capacity profiles of debtor groups classified by the borrower characteristics such as income, age, occupation, credit scoring, and the type of creditor business companies, we investigate the impact of interest rate and income changes on debt service-to-income ratios (DTIs) and default rates of respective debtor groups. Empirical results indicate that debt service burdens are relatively high for low income wage earners, high income self-employed, low income capital and card loan holders, and high income mutual savings loan holders. We also find that debtors from multiple financial companies are particularly weak in their debt service capacity. The scenario analysis indicates that financial companies, with the current level of capital buffers, may be able to absorb negative consequences arising from the increase in DTIs and loan default rates if the interest rate and income changes remain modest. However, the negative consequences may fall disproportionately on non-bank financial companies such as capital, credit card, and mutual savings banks, whose debtors' DTIs are already high. We also find that the refinancing risk of household debt is relatively high in Korea as more than half of household mortgage debts are bullet loans. As the DTIs of mortgage loan holders are already high, under the current DTI regulation, mortgage loans may not be readily refinanced especially when the interest rate rises. Disruptions in mortgage loan refinancing may put downward pressure on housing prices, which may in turn magnify refinancing risk under the current loan-to-value (LTV) regulation. Overall our analysis suggests that, for more effective monitoring of household debt risk, it is necessary to combine existing surveillance schemes based on macro aggregate indicators with more comprehensive and detailed risk analyses based on micro individual data.

• Management & Information Systems Review
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• v.36 no.4
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• pp.21-32
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• 2017

Motivated by the recent cases of negligent social responsibility as manifested by foreign luxury fashion brands in Korea, this study investigates whether agency costs depend on the sustainability of different types of corporate governance. Agency costs refer either to vertical costs arising from the relationship between stockholders and managers, or to horizontal costs associated with the potential conflicts between majority and minority stockholders. The firms with luxury fashion brand could spend large sums of money on maintenance of magnificent brand image, thereby increasing the agency cost. On the contrary, the firms may hold down wasteful spending to report a gaudily financial achievement. This results in mitigation of the agency cost. Agency costs are measured by the value of the principal component. First, three ratios are constructed: asset turnover, operating expense to sales, and earnings before interest, tax, and depreciation. Then, the scores of each of these ratios for individual firms in the sample are differenced from the ratios for the benchmark firm of S-OIL. S-OIL was designated as the best superior governance model firm for 2013 by CGS. We perform regression analysis of each agency cost index, luxury fashion brand dummy and a set of control variables. The regression results indicate that the agency costs of the firms with luxury fashion brand exceed those of control group in the fashion industry in the part of operating expenses, but the agency cost falls short of those of control group in the part of EBITD, thus the aggregate agency costs are not differential of those of the control group. In sensitivity test, the results are same that the agency cost of the firms are higher than those of the matching control group with PSM(propensity matching method). These results are corroborated by an additional analysis comparing the group of the companies with the best brands with the control group. The results raise doubts about the effectiveness of management of the firms with luxury fashion brand. This study has a limitation that the research has performed only for 2013 and this paper suggests that there is room for improvement in the current research methodology.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.6
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• pp.110-118
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• 2017

This study investigated empirically one of the controversial subjects in modern finance, in that there is an optimal level of capital structure for KOSPI-listed firms in the Korean capital market. Given the major theories on the capital structure, such as Myers' pecking order, trade-off, and agency cost ones, this study applied an analysis of covariance models in parametric and non-parametric statistical methods. In particular, two covariates to control for the possible effects of trade-off and agency cost, were employed separately in each corresponding model, while the other proxy for pecking order rationale was adopted in previous research [1] to conduct inter- and intra-industry analyses. Based on the outcomes obtained from the study, it was demonstrated empirically that there are optimal capital structures for firms in the sample industries at the inter-industry level, whereas statistical differences indicating non-existence of an optimal point, were revealed within the industry. Accordingly, these findings suggest a new vision to potential investors that firms in the domestic market may have financial opportunities to increase their value by gradually adjusting the leverage ratios in terms of the intra-industry perspective.