• 한국벤처창업학회:학술대회논문집
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• 2010.08a
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• pp.139-160
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• 2010

On July 21st in 2005, People's Bank of China (PBOC) turned the currency peg against the U.S. dollar into managed currency system based on a basket of unnamed currencies under China's exchanged rate regime. This change means that China's enterprises are not free from currency fluctuations. The purpose of this study is to analyze the relations between Renminbi fluctuations in the floating exchange rate and financial results of venture companies. The process and outcomes of this study are as follows, First, in order to measure the financial results of venture companies, I choose venture companies in Shandong Province listed on the Shanghai Stock Exchange (SSE) at random and several quarter financial sheets according to safety ratios, profitability ratios, growth ratios, activity ratios. Second, I arrange the daily Renminbi exchange rate data announced from July 21st, 2005 to December 31st, 2008 by PBOC into the quarterly data. Third, in order to confirm the relations between Renminbi fluctuations and financial results of venture companies, I carry out Pearson's correlation analysis. As a result, the revaluation of the Chinese Renminbi has weakly negative effects on debt ratio, total assets turnover ratio and equity turnover ratio in statistics. But the revaluation of the Chinese Renminbi is not related to other financial index in statistics. The result of this study is that the revaluation of the Chinese Renminbi has little influence on the export and import of Chinese venture companies and certifies the fact that Chinese venture companies have much foreign currency assets. In addition to avoid the currency exposure risk, this study shows the effective method about currency exposure risk which adjusts proportion of Renminbi to foreign currency.

• 한국벤처창업학회:학술대회논문집
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• 2018.11a
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• pp.165-175
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• 2018

Since the 1997 financial crisis, large scale unemployment and poverty have become serious, and public and social job creation projects have been carried out. However, with the limitations of low-wage and short-term jobs, the need for long-term and high quality jobs gradually began to attract attention. In recent years, social enterprises have grown both quantitatively and qualitatively and interest in social enterprises has increased. And also it is interested in the determinants of success and failure of social enterprises in the academic field. In this study, we examined the effects of social enterprise characteristics on financial and social performance, and we analyzed empirically by using social enterprises registered in the Korea Social Enterprise Agency. The financial performance of the social enterprise is measured by the net income ratio, operating income ratio, and the return on asset. The social performance of the social enterprise is measured by total number of workers and the employment rate of the vulnerable social groups. The characteristics of the social enterprise include the CEO characteristics (gender, age, experience in operating the social enterprise), the firm size, and the elapsed time of the authentication. The results of the empirical analysis are as follows. First, as a result of analysis for the effect on financial performance, we found that the financial performance have a statistically significant positive relationship with firm size, organizational form, government subsidies and capital adequacy ratio. And it is found that the social performance have a statistically significant negative relationship with CEO age, credit debt dependence. Second, as a result of analysis for the effect on social performance, we foumd that total number of workers have a significant positive relationships with CEO gender, CEO age, and firm size, government subsidies, while total number of workers have a significant negative relationship with certification type and industry dummy. On the other hand, the employment rate of the vulnerable social groups have a siginificant positive relationship with CEO gender and certification type and It have not statistically significant relationship with the government subsidies and the firm size.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.2
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• pp.61-72
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• 2019

Since the financial crisis in 1997, large scale unemployment and poverty have become serious, but there has been a surge in public and social job creation projects. However, with the limitations of low-wage and short-term jobs, the need for long-term, high quality jobs gradually began to garner attention. In recent years, social enterprises have grown both quantitatively and qualitatively and interest in social enterprises has increased; more specifically, scholars are interested in the determinants of success and failure of social enterprises in the academic field. In this study, we examined the effects of social enterprise characteristics on financial and social performance. In particular, we empirically analyzed social enterprises registered in the Korea Social Enterprise Agency. The financial performance of the social enterprise was measured using the net income ratio, operating income ratio, and the return on asset. The social performance of the social enterprise was measured by the total number of workers and the employment rate of vulnerable social groups. The characteristics of the social enterprise included CEO characteristics (gender, age, experience in operating the social enterprise), firm size, and the elapsed time of authentication. The results of the empirical analysis are as follows. First, as a result of analysis for the effect on financial performance, we found that the financial performance has a statistically significant, positive relationship with firm size, organizational form, government subsidies, and capital adequacy ratio. And we found that the social performance has a statistically significant, negative relationship with CEO age and credit debt dependence. Second, as a result of analysis for the effect on social performance, we found that the total number of workers had a significant, positive relationship with CEO gender and CEO age, as well as firm size, government subsidies; whereas the total number of workers had a significant, negative relationship with certification type and industry dummy. Comparatively, the employment rate of the vulnerable social groups had a significant, positive relationship with CEO gender and certification type, but there was no statistically significant relationship with the government subsidies or firm size.

• 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 Korean Academy of Nursing
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• v.22 no.4
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• pp.636-652
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• 1992

This study identified the impact of childhood cancer on the Korean family. The purpose was to contribute knowledge for family nursing and pediatric hospice care practice with sick children and their families. This descriptive study was conducted during a 6 month period with children who were being treated for cancer at six university hospitals in Seoul. The data were gathered from members of 68 families ; 24(Group A), with a child newly diagnosed with cancer : 27(Group B), with a child under treatment and without complications, and 17 (Group C), with a child in relapse. Medical records, structured questionnaires and interviews were used for data collection. The questionnaires and interview schedules had been used previously in Martinson's research in the USA and China. The findings, conclusions, and suggestions are as follows. 1. The impact of childhood cancer on the family. Members of the family experienced fear, helplessness, guilty feelings, and anger at the time of the initial diagnosis and at relapse. Mothers complained of headache, anorexia and poor appetite, weight loss, sleep disturbance, and bad dreams. Many of the fathers either lost or changed jobs, and all working mothers stopped working. Half the parents reported changes in their marital relationships such as frequent quarrels but also stronger unity. Family members perceived cancer as the most frightening disease. Change in their world view was expressed as living on faith understanding suffering, determining to live a better life, wanting to live an upright life and valuing health as the most important. Religious activities are found most helpful through this difficult experience. Financial debt due to the treatment and care of the sick child, burdened 22 families. The above mentioned impact was most evidant in Group B(those presently undergoing treatment) and Group C(those in relapse). Findings indicate that nursing care should embrace the family of a child who is being treated for cancer. 2. Characteristics of the child with cancer The majority of the children in this sample had a diagnosis of leukemia. Their mean age was 6.8 and the ratio of boys to girls was 1.12 ; 1. The mean hospitalization frequency was 13.5 times and the mean duration of illness was 16.8 months. Most of 1.he children perceived cancer as the most frightening disease ; 32.7% of the children described their sickness as serious. Children in Group C were hospitalized more frequently, stayed in hospital for longer periods, and expressed their sickness as quite serious more often than the other two groups. These findings indicate how much comprehensive pediatric hospice nursing care services are needed along with relevant research and nursing education. 3. Characteristics of the families. The mean age of the father was 39.5 and the mother, 36,6 ; they are in their most productive life period. Mothers especially expressed feelings of financial uneasiness and powerlessness about giving up their jobs, and guilty feelings for not providing enough care and concern to other children due to taking care of the sick one. The burden of caring for the sick child can bring negative changes in family dynamics which they think provoke potential health problems in members of the family These findings suggest a need for nursing support and counselling resources. Findings also suggest the need for ethical inquiry about such questions as who should give information to the child in regard to diagnosis and prognosis, when, and how. Other suggestions included : 1) Quality health care for childhood cancer such as home care and pediatric hospice programs should be established. 2) Special and practical consideration for long-term patients should be made in the present insurance coverage. The reimbursement period for long-term patients should be lengthened. 3) Further in-depth qualitative studies are needed. 4) Education programs including guided practice experience for pediatric hospice care practitioners are needed.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.2
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• pp.193-208
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• 2020

In this study, we investigate the effects of technology innovation investments and employment on credit ratings of very small start-up businesses using the data period of 2009 till 2015 test two hypotheses: asymmetric information hypothesis or competence hypothesis. We use financial and non-financial data of 51,903 observations of 12,028 small businesses from a database of a commercial bank and fixed effects panel models and two-stage instrumental variable models. We find that in the short-run small size startups show lower credit ratings than non-startups, and that both technology innovation activities and employment capability improve their credit ratings. In the long-run, technology innovation investments do not improve their credit ratings of later years while employment capability improve their credit ratings of the subsequent year. In addition, the age of startups improves their credit ratings of the current year and until the subsequent two years while employee productivity, fixed ratio and ROA positively affect their credit ratings for up to three years. However, short-term and overall debt ratios, cost of borrowings and firm-size negatively affect their credit ratings for up to three years. The results of the study on credit ratings suggest that credit rating agencies seem to consider both technology innovation activities and employment capability in the credit ratings of small start-ups as 'competence factors' rather than 'asymmetric information factors' with inefficiency and cost burdens. The results also suggest that we must find ways to reflect properly the severe asymmetric information of the early-stage start-ups, and technology innovation activities and employment capability in the credit rating formula.

• Management & Information Systems Review
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• v.23
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• pp.41-74
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• 2007

In Korea, corporate merger activities were activated since 1980, and nowadays(particuarly since 1986) the changes in domestic and international economic circumstances have made corporate managers have strong interests in merger. Korea and America have different business environments and it is easily conceivable that there exists many differences in motives, methods, and effects of mergers between the two countries. According to recent studies on takeover bids in America, takeover bids have information effects, tax implications, and co-insurance effects, and the form of payment(cash versus securities), the relative size of target and bidder, the leverage effect, Tobin's q, number of bidders(single versus multiple bidder), the time period (before 1968, 1968-1980, 1981 and later), and the target firm reaction (hostile versus friendly) are important determinants of the magnitude of takeover gains and their distribution between targets and bidders at the announcement of takeover bids. This study examines the theory of takeover bids, the status quo and problems of merger in Korea, and then investigates how the announcement of merger are reflected in common stock returns of bidding firms, finally explores empirically the factors influencing abnormal returns of bidding firms' stock price. The hypotheses of this study are as follows ; Shareholders of bidding firms benefit from mergers. And common stock returns of bidding firms at the announcement of takeover bids, shows significant differences according to the condition of the ratio of target size relative to bidding firm, whether the target being a member of the conglomerate to which bidding firm belongs, whether the target being a listed company, the time period(before 1986, 1986, and later), the number of bidding firm's stock in exchange for a stock of the target, whether the merger being a horizontal and vertical merger or a conglomerate merger, and the ratios of debt to equity capital of target and bidding firm. The data analyzed in this study were drawn from public announcements of proposals to acquire a target firm by means of merger. The sample contains all bidding firms which were listed in the stock market and also engaged in successful mergers in the period 1980 through 1992 for which there are daily stock returns. A merger bid was considered successful if it resulted in a completed merger and the target firm disappeared as a separate entity. The final sample contains 113 acquiring firms. The research hypotheses examined in this study are tested by applying an event-type methodology similar to that described in Dodd and Warner. The ordinary-least-squares coefficients of the market-model regression were estimated over the period t=-135 to t=-16 relative to the date of the proposal's initial announcement, t=0. Daily abnormal common stock returns were calculated for each firm i over the interval t=-15 to t=+15. A daily average abnormal return(AR) for each day t was computed. Average cumulative abnormal returns($CART_{T_1,T_2}$) were also derived by summing the $AR_t's$ over various intervals. The expected values of $AR_t$ and $CART_{T_1,T_2}$ are zero in the absence of abnormal performance. The test statistics of $AR_t$ and $CAR_{T_1,T_2}$ are based on the average standardized abnormal return($ASAR_t$) and the average standardized cumulative abnormal return ($ASCAR_{T_1,T_2}$), respectively. Assuming that the individual abnormal returns are normal and independent across t and across securities, the statistics $Z_t$ and $Z_{T_1,T_2}$ which follow a unit-normal distribution(Dodd and Warner), are used to test the hypotheses that the average standardized abnormal returns and the average cumulative standardized abnormal returns equal zero.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.479-490
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• 2021

As a part of Korean accounting reforms through the improvement of the accounting and audit related systems, the amendment bill of 'Act on External Audit of Stock Compamies's was passed in the Natinal Assembly plenary session in 2017, the amended act has been enforced except some regulations since the business year on November 1, 2018, and all the amended matters will be applied from the business year of 2024. The reasons for auditor designation in 2019 are 'pre-IPO' 331 companies, 220 periodic designation companies, 197 companies that had operating loss for three consecutive years, 112 companies with issues for administration, 108 companies with excessive debt ratio and 66 companies with no auditors. Regarding the reasons for the increase of auditor designation, 475 companies were increased in accordance with the new designation standard by the amended bill of Act on External Audit of Stock Companies, 114 companies were increased due to the abolition of the considered designation system of companies to be listed, and 90 companies were increased based on the increase of listed companies incorporated to issues for administration. In 2020, 462 companies had periodical designation (434 listed, 28 non-listed), adding 242 companies (110%) over a year. In terms of direct designation, 'pre-IPO' accounted for the most (362 companies), followed by '3 consecutive years of operating loss' (245 companies), then by companies with administration issues (133 companies), and CEO & largest sharholder replacement. Regarding the designation of auditors according to accounting firms in 2020, A group that includes(top 4) accounting firms(Samil, Samjeong, Hanyeong, Anjin) had 526 companies(34.6%), which ia an incease of 72 companies from the previous year(454 companies, 37.1%), but the weight decreased by 2.5%.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.1-32
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• 2018

In addition to stakeholders including managers, employees, creditors, and investors of bankrupt companies, corporate defaults have a ripple effect on the local and national economy. Before the Asian financial crisis, the Korean government only analyzed SMEs and tried to improve the forecasting power of a default prediction model, rather than developing various corporate default models. As a result, even large corporations called 'chaebol enterprises' become bankrupt. Even after that, the analysis of past corporate defaults has been focused on specific variables, and when the government restructured immediately after the global financial crisis, they only focused on certain main variables such as 'debt ratio'. A multifaceted study of corporate default prediction models is essential to ensure diverse interests, to avoid situations like the 'Lehman Brothers Case' of the global financial crisis, to avoid total collapse in a single moment. The key variables used in corporate defaults vary over time. This is confirmed by Beaver (1967, 1968) and Altman's (1968) analysis that Deakins'(1972) study shows that the major factors affecting corporate failure have changed. In Grice's (2001) study, the importance of predictive variables was also found through Zmijewski's (1984) and Ohlson's (1980) models. However, the studies that have been carried out in the past use static models. Most of them do not consider the changes that occur in the course of time. Therefore, in order to construct consistent prediction models, it is necessary to compensate the time-dependent bias by means of a time series analysis algorithm reflecting dynamic change. Based on the global financial crisis, which has had a significant impact on Korea, this study is conducted using 10 years of annual corporate data from 2000 to 2009. Data are divided into training data, validation data, and test data respectively, and are divided into 7, 2, and 1 years respectively. In order to construct a consistent bankruptcy model in the flow of time change, we first train a time series deep learning algorithm model using the data before the financial crisis (2000~2006). The parameter tuning of the existing model and the deep learning time series algorithm is conducted with validation data including the financial crisis period (2007~2008). As a result, we construct a model that shows similar pattern to the results of the learning data and shows excellent prediction power. After that, each bankruptcy prediction model is restructured by integrating the learning data and validation data again (2000 ~ 2008), applying the optimal parameters as in the previous validation. Finally, each corporate default prediction model is evaluated and compared using test data (2009) based on the trained models over nine years. Then, the usefulness of the corporate default prediction model based on the deep learning time series algorithm is proved. In addition, by adding the Lasso regression analysis to the existing methods (multiple discriminant analysis, logit model) which select the variables, it is proved that the deep learning time series algorithm model based on the three bundles of variables is useful for robust corporate default prediction. The definition of bankruptcy used is the same as that of Lee (2015). Independent variables include financial information such as financial ratios used in previous studies. Multivariate discriminant analysis, logit model, and Lasso regression model are used to select the optimal variable group. The influence of the Multivariate discriminant analysis model proposed by Altman (1968), the Logit model proposed by Ohlson (1980), the non-time series machine learning algorithms, and the deep learning time series algorithms are compared. In the case of corporate data, there are limitations of 'nonlinear variables', 'multi-collinearity' of variables, and 'lack of data'. While the logit model is nonlinear, the Lasso regression model solves the multi-collinearity problem, and the deep learning time series algorithm using the variable data generation method complements the lack of data. Big Data Technology, a leading technology in the future, is moving from simple human analysis, to automated AI analysis, and finally towards future intertwined AI applications. Although the study of the corporate default prediction model using the time series algorithm is still in its early stages, deep learning algorithm is much faster than regression analysis at corporate default prediction modeling. Also, it is more effective on prediction power. Through the Fourth Industrial Revolution, the current government and other overseas governments are working hard to integrate the system in everyday life of their nation and society. Yet the field of deep learning time series research for the financial industry is still insufficient. This is an initial study on deep learning time series algorithm analysis of corporate defaults. Therefore it is hoped that it will be used as a comparative analysis data for non-specialists who start a study combining financial data and deep learning time series algorithm.