• Asia-Pacific Journal of Business
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• v.14 no.2
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• pp.51-61
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• 2023

Purpose - Since COVID-19, the government's expansion of liquidity to stimulate the economy has resulted in an increase in private debt and an increase in asset prices of such as real estate and stocks. The recent sharp rise of the US Federal fund rate and tapering by the Fed have led to a fast rise in domestic interest rates, putting a heavy burden on the Korean economy, where the level of household debt is very high. Excessive household debt might have negative effects on the economy, such as shrinking consumption, economic recession, and deepening economic inequality. Therefore, now more than ever, it is necessary to identify the causes of the increase in household debt. Design/methodology/approach - Main methodology is regression analysis. Dependent variable is household loans from depository institutions. Independent variables are consumer price index, unemployment rate, household loan interest rate, housing sales price index, and composite stock price index. The sample periods are from 2017 to May 2022, comprising 72 months of data. The comparative analysis period before and after COVID-19 is from January 2017 to December 2019 for the pre-COVID-19 period, and from Jan 2020 to December 2022 for the post-COVID-19 period. Findings - Looking at the results of the regression analysis for the entire period, it was found that increases in the consumer price index, unemployment rate, and household loan interest rates decrease household loans, while increases in the housing sales price index increase household loans. Research implications or Originality - Household loans of depository institutions are mainly made up of high-credit and high-income borrowers with good repayment ability, so the risk of the financial system is low. As household loans are closely linked to the real estate market, the risk of household loan defaults may increase if real estate prices fall sharply.

• Health Policy and Management
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• v.12 no.2
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• pp.43-75
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• 2002

This study focuses on the factors that make the financial structure of hospitals in Korea different, and on recommended courses of action that could be very helpful to hospitals in maintaining a sound financial structure. Data used in this study were collected from 132 hospitals with complete general data of present conditions as well as financial statements. They were chosen from the 174 hospitals that passed the standardization audit undertaken by the Korean Hospital Association from 1996 to 2000 for the purpose of accrediting training hospitals. The dependent variable in this study is financial structure. It consists of liabilities as against total assets (total liabilities to total assets, short-term liabilities to total assets, long-term liabilities to total assets, short-term borrowings to total assets, long-term borrowings to total assets). The independent variables are ownership type, hospital type, location, whether or not a representative is a director of the hospital, the possibility of changing a hospital director, bed size, period of establishment, asset structure, profitability, growth, tax shields, business risk, competition. The factors that appear to have the strongest impact on the liabilities to total assets of all the hospitals sampled are ownership type, hospital type, profitability, tax shields, and business risk. It was found that not-for-profit private hospitals and for-profit private hospitals have more liabilities than public hospitals, and tertiary medical institutions have less liabilities than the secondary general hospitals. Moreover, hospitals earning more at the expense of high business risk have a distinct tendency to lower liabilities. Concerning the current ratio, it was found that factors such as ownership type, hospital type, period of establishment, asset structure, and business risk are the more significant variables. The current ratio of public hospitals is higher than that of both not-for-profit private hospitals and for-profit private hospitals, and the current ratio of tertiary medical institutions is higher than that of general hospitals. As business risk is higher in hospitals compared to other businesses, the current ratio becomes higher; this is because it is assumed that for fear of bankruptcy, hospitals lessen liabilities to total assets. On the other hand, as hospitals become older, the fixed assets to total assets become lower. It is remarkable that in hospitals, the factors affecting liabilities to total assets have an opposite regression coefficient sign against factors affecting current ratio. It brings out the same results borne out by the old financial theories and researches, in which a lot of the liabilities of hospitals are considered as the cause of worsening liquidity. Therefore, it is very important for hospitals to maintain a sound financial structure in order to survive using the rational acquisition and maintenance of capital.

• Management & Information Systems Review
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• v.36 no.4
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• pp.53-80
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• 2017

The purpose of this study is to examine the determinants of the number of bank relationships in Korea. Firm-specific determinants considered here include leverage, size, age, return on asset, investment grade, tangibility, liquidity, R&D expenditure. We estimate the effects of these variables, and compare the results with those from previous studies performed for other economies. Concerning the effects of business cycle, we find that the business cycle is an important factor in determining the number of bank relationships. The number of bank relationships varies over the business cycle, and we notice a counter-cyclical behavior, which means the number decreases during economic expansions and increases during contractions. This result can be interpreted as a result of firms' diversification of borrowings into multiple banks in order to reduce the liquidity risk during the recession. In the subsets, however, the number of bank relationships for large firms is stable regardless of the business cycle. Unlisted firms, non-chaebol, and low credit quality firms which have relatively limited access to alternative sources of financing show counter-cyclical behavior. Finally, such phenomena is not observed in the non-competitive credit market, while they show a counter-cyclical behavior in the competitive credit market.

• The Korean Journal of Financial Management
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• v.24 no.3
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• pp.91-131
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• 2007

In the paper, the effects of sidecar on index arbitrage trading and non-index arbitrage trading in the Korean stock market are examined. The analyses of return, volatility, and liquidity dynamics illustrate that there are no distinct differences for index arbitrage group and non-index arbitrage group surrounding the sidecar events. For further analysis, we construct pseudo-sidecar sample and analyse the effects of the actual sidecar and pseudo-sidecar on arbitrage sample and non-index arbitrage sample. The result of analysis using pseudo-sidecar shows that the differences between index arbitrage group and non-index arbitrage group are larger in pseudo-sidecar sample than in actual sidecar sample. This means that former results can be explained by temporary order clustering in one side before and after the event. Sidecar has little effect on non-index arbitrage group, however, it has relatively large effect on arbitrage group. These results imply that it needs to redesign the sidecar system of the Korean stock market which applies for all program trading including arbitrage and non-index arbitrage trading.

• Journal of Climate Change Research
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• v.9 no.4
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• pp.325-342
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• 2018

The Korea Emissions Trading Scheme ( K-ETS), which manages roughly 70% of the greenhouse gas emissions in South Korea, was initiated in 2015, after implementation of its 1st basic plan and the 1st allocation plan (2014) for the 1st phase (2015-2017). During the three and a half years since the launch of K-ETS, there have been critical policy change such as adjustment of the institutions involved, development and revision of the 2030 national GHG reduction roadmap, and change in the allocation plans. Moreover, lack of liquidity and fluctuation of carbon prices in the K-ETS market during this period has forced the Korean government to adjust the flexibility mechanism and auction permits of the market stability reserve. To evaluate the policy change in the K-ETS regarding conformance to its objectives, this study defines three objectives (Environmental Effectiveness, Cost Effectiveness and Economic Efficiency) and ten indicators. Evaluation of Environmental Effectiveness of K-ETS suggests that the national GHG reduction roadmap, coverage of GHG emitters and credibility of MRV positively affect GHG mitigation. However, there was a negative policy change implemented in 2017 that weakened the emission cap during the 1st phase. In terms of the Cost Effectiveness, the K-ETS policies related to market management and flexibility mechanism (e.g. banking, borrowing and offsets) were improved to deal with the liquidity shortage and permit price increase, which were caused by policy uncertainty and conservative behavior of firms during 2016-2018. Regarding Economic Efficiency, K-ETS expands benchmark?based allocation and began auction-based allocation; nevertheless, free allocation is being applied to sectors with high carbon leakage risk during the 2nd phase (2018-2020). As a result, it is worth evaluating the K-ETS policies that have been developed with respect to the three main objectives of ETS, considering the trial?and?error approach that has been followed since 2015. This study suggests that K-ETS policy should be modified to strengthen the emission cap, stabilize the market, expand auction-based allocation and build K-ETS specified funds during the 3rd phase (2021-2025).

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.348-362
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• 2021

Real estate investment behavior factors are divided into profitability, risks (stability), liquidity, and regulation (deregulation) factors. The sub-attributes of the investment behavior factors are generally formative indicators. Unlike reflection indicators, formative indicators can identify not only the influence of investment behavior factors on dependent variables, but also the influence of sub-attributes on dependent variables. Therefore, theoretical and practical needs of comparing the influences of factors and sub-attributes on dependent variables has been suggested. In this study, in order to provide information that help marketing for lots shopping building, both the causality between investment behavior factors and investment intention and the causality between sub-attributes and investment intention were comparatively studied for each of the three investor groups: the whole group, the group with high investment intention and the group with low investment intention. For this purpose, a survey and multiple regression analyses were conducted on 237 existing investors in the customer DB of a company that have been developing and selling lots shopping building in the metropolitan area and Sejong City. At the factor level, the effects of profitability and regulation were significant in the whole group and the group with low investment intention, but the effects of risk and liquidity were significant in the group with high investment intention. At the sub-attribute level, all three groups showed different results.

• Management & Information Systems Review
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• v.29 no.2
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• pp.1-25
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• 2010

This study attempted to empirically test the determinants of stock returns in Korean stock market applying multi-factor model proposed by Haugen and Baker(1996). Regression models were developed using 16 variables related to liquidity, risk, historical price, price level, and profitability as independent variables and 690 stock monthly returns as dependent variable. For the statistical analysis, the data were collected from the Kis Value database and the tests of forecasting power in this study minimized various possible bias discussed in the literature as possible. The statistical results indicated that: 1) Liquidity, one-month excess return, three-month excess return, PER, ROE, and volatility of total return affect stock returns simultaneously. 2) Liquidity, one-month excess return, three-month excess return, six-month excess return, PSR, PBR, ROE, and EPS have an antecedent influence on stock returns. Meanwhile, realized returns of decile portfolios increase in proportion to predicted returns. This results supported previous study by Haugen and Baker(1996) and indicated that firm-characteristic model can better predict stock returns than CAPM. 3) The firm-characteristic model has better predictive power than Fama-French three-factor model, which indicates that a portfolio constructed based on this model can achieve excess return. This study found that expected return factor models are accurate, which is consistent with other countries' results. There exists a surprising degree of commonality in the factors that are most important in determining the expected returns among different stocks.

• 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.

• International Commerce and Information Review
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• v.15 no.3
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• pp.289-310
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• 2013

In the past, an policy measures for the promotion of the export has actively used trade finance, but also in its effect there is no doubt. However, in 2008 the bankruptcy of Lehman Brothers triggered the global financial crisis. As a result, the need to effectively manage liquidity risk posed, and was a debut for Basel III. Focusing on trade finance banks are being made. Domestic commercial banks have not been able not utilize various trade finance techniques. In these situations, the introduction of Basel III can discourage trade finance. Therefore, responses should be prepared for it. Therefore, this study analyzes the status of trade finance system. And international regulation of the financial market are investigated for changes. Based on this, the development direction of Korea's trade finance is proposed.

• KDI Journal of Economic Policy
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• v.28 no.2
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• pp.239-262
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• 2006

The paper suggests that there has been a shift in the allocation of bank credit from large firms to small firms before and after the economic crisis. The paper also suggests that the improved lending practices of financial institutions, at least partially, contributed to this shift of corporate loans from large firms to small firms. Comparing the periods before and after the economic crisis also suggests that some important changes occurred to the corporate bond market. The effect of firm size on the corporate bond market differs before and after the economic crisis. Before the crisis, the larger the firms, the more they could borrow in the corporate bond market. However, after the crisis, it is not the case. The following interpretation could be put forward. Before the crisis, investors in the corporate bond market expected that the government would rescue large firms if they face the risk of bankruptcies. However, the collapse of Daewoo Group in 1999 shattered the TBTF (Too Big To Fail) myth of the public. The liquidity crisis of Hyundai Group in 2000-2001 reinforced the disintegration of the TBTF myth.