• Journal of Korea Society of Industrial Information Systems
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• v.25 no.2
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• pp.101-113
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• 2020

Since the number of social enterprises have been increased more than double over the past five years, the determinant of their growth by regional comparison is examined in this paper. In terms of the sale determinant, experimental results show that the total number of workers, business history, and capital intensity in Seoul metropolitan area, the total number of workers, business history, and capital intensity in Gyeongsang area, the total number workers, business history, and the dependence on government subsidies in Chungcheong area, the total number of workers, and the capital intensity in Jeolla/Jeju area have showed positive effects. In terms of asset determinants, experimental results show that the total number of workers, business history, and capital in Seoul metropolitan area, the capital in Gangwon area, the total number of workers, business history, and capital in Gyeongsang area, the total number of workers, business history, and capital intensity in Chungcheong area, the total number of workers, capital, and capital intensity in Jeolla/Jeju area have showed positive effect.

• International Area Studies Review
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• v.12 no.2
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• pp.149-173
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• 2008

This paper empirically studies the relationship between R&D expenditures and firms value. First, we can conjecture that R&D expenditures are enhancing the firms value. Such findings depend on an existing research, which R&D expenditures are intangible asset rather than expenses. Although, under U.S. accounting standards, financial statements do not report intangible assets but costs. Second, we can conjecture that short-term, the rate of increase in R&D expenditures had negative influence on firms valuation, because such findings indicates that R&D spending of costs incur mis-pricing. But long-term, consistently R&D expenditures may attract investors on the stock market. Third, lately firms focus on capital efficiency management, such a firms R&D expenditures incur high ROE. Generally investors put too much confidence in capital efficiency management and high ROE may attract investors on the stock market. Finally, High-Tech through the R&D investment improve firms competitive advantage, by competitive advantage, firms have reduced cost and raised productivity in the end improve firms value.

• The Journal of the Korea Contents Association
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• v.19 no.5
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• pp.287-295
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• 2019

This study was conducted to analyze firm survival rate and impact factors of survival of Foreign-invested Companies in Korea which is between 2006 and 2017. An empirical analysis of the survival factors of firms used explanatory variables such as characteristics of the and 3 firm dummy and 2 firm factors, financial variables of 3 profitability and 3 stability factors. The Kaplan-Meier method was chosen to perform analyses on the survival rates, Cox Proportional Hazard Model took to conduct on the impact factors. As a result of the impact factors of Foreign-invested Companies survival, Ownership (OS), Labour (NE ) of characteristics of the firm had positive effects. The Gross Sales Profit (GSP), Net Profit (NP ) and Operating Profit (OP ) of the financial characteristics had a positive effect. Additional Asset (LA ) had positive effects and Capital (LC), Debt (LB ) had a negative effect. Other factors did not produce significant results.

• Journal of Korea Trade
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• v.25 no.1
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• pp.65-88
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• 2021

Purpose - This paper aims to examine the determinants of SWFs' investment in Asian countries and to identify consistent investment patterns of SWFs in specific target firms from Asia, particularly China and South Korea. Design/methodology - This study extends the Tobin's Q model to examine the relationship between SWF investments in target firms and their returns with other firm-level control variables. We collect consistent data on SWF investments and the matched firm-level data on target firms, which of observation is 1,512 firms (333 in South Korea and 1,179 in China) targeted by 20 SWF sources during 1997-2017. The panel random effect model is used to estimate the extended Tobin's Q model. The robustness of the estimations is tested by the simultaneous equation models and the panel GEE model. Findings - The evidence shows that sovereign wealth funds are more inclined to invest in the financial sector with a monopoly position and in large firms with higher growth opportunity and superior cash asset ratios in China. In contrast to their investments in China, sovereign wealth funds in South Korea prefer to invest in strategic sectors, such as energy and information technology, and in large firms with high performance and low leverage. Sovereign wealth funds' investments tend to significantly improve the target firm's performance measured by sales growth and returns in both Korea and China. Originality/value - The existing literature focuses on examining the determination of SWFs investment in the developed countries, such as Europe and the United States. Our paper contributes to the literature in three ways; first, we analyzes case studies of SWF investments in Asian markets, which are less developed and riskier. Second, we examine whether the determination of SWF investment in Asian target firms depends on the different time periods, on types of sources of SWFs, and on acquiring countries. Third, our research uses vast sample data on target firms in longer time periods (1997-2017) than other previous studies on the SWFs for Asian markets.

• Journal of Korea Trade
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• v.24 no.7
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• pp.38-53
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• 2020

Purpose - A financial crash triggers asset fire sales by foreign investors and, as a consequence, the price of domestic assets severely decreases. Domestic investors take advantage of these low prices by replacing foreign assets with domestic assets, which helps to alleviate the liquidity shock caused by foreigners. However, is the amount of capital retrenchment by domestic investors sufficient to protect the Korean economy from capital stop by foreign investors during financial crisis? This paper answers this question and suggests the implications of this phenomenon for the Korean economy. Design/methodology - We estimate the associations between capital stop and retrenchment and various financial crises such as banking, currency, debt, and inflation crises using the complementary log-log model. Specifically, we use data of gross capital flows to differentiate between the role of foreign and domestic investors in financial markets. Capital stop and retrenchment designate a sharp decrease in gross capital inflows and outflows, respectively. Findings - Capital stop is significantly associated with financial crises, especially currency and debt crises. This implies that increased risk aversion during times of financial turmoil encourages foreign investors to retrench their investments, worsening liquidity shocks. Conversely, capital retrenchment is not significantly associated with such crises. The results show that, although financial crises reduce gross capital outflows, the reduction is not as large as that with capital inflows. Originality/value - The contribution of this paper is threefold. First, this study investigates how domestic investors behave during times of financial distress by studying gross capital flows-not net capital flows. Second, we concentrate on sharp changes in capital flows during crises. Third, we examine the associations between capital stop and retrenchment and financial crises in general, not specific events.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.5
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• pp.659-666
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• 2023

Characterized by boom-and-bust cycles, low entry barriers, and an almost perfectly competitive structure, the construction industry presents a unique challenge for the survival and growth of its constituent companies. A crucial aspect of this challenge is the ongoing monitoring of their financial health and business performance. To understand the typical financial and operational status of construction companies, this study analyzes the financial statements of 6,252 such companies, all of which have undergone at least one external audit between 2000 and 2019. These statements were used to develop combined financial profiles and derive industry averages. The findings indicate that the construction industry experiences limited growth in sales and profitability. High leverage ratios can jeopardize financial stability, pushing companies to seek production efficiency, such as enhancing gross asset turnover. This tendency has been particularly noticeable in the aftermath of the global financial crisis in 2008.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.195-220
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• 2018

In this study, we conducted an empirical analysis of the factors that affect the change of Bitcoin Closing Price. Previous studies have focused on the security of the block chain system, the economic ripple effects caused by the cryptocurrency, legal implications and the acceptance to consumer about cryptocurrency. In various area, cryptocurrency was studied and many researcher and people including government, regardless of country, try to utilize cryptocurrency and applicate to its technology. Despite of rapid and dramatic change of cryptocurrencies' price and growth of its effects, empirical study of the factors affecting the price change of cryptocurrency was lack. There were only a few limited studies, business reports and short working paper. Therefore, it is necessary to determine what factors effect on the change of closing Bitcoin price. For analysis, hypotheses were constructed from three dimensions of consumer, industry, and macroeconomics for analysis, and time series data were collected for variables of each dimension. Consumer variables consist of search traffic of Bitcoin, search traffic of bitcoin ban, search traffic of ransomware and search traffic of war. Industry variables were composed GPU vendors' stock price and memory vendors' stock price. Macro-economy variables were contemplated such as U.S. dollar index futures, FOMC policy interest rates, WTI crude oil price. Using above variables, we did times series regression analysis to find relationship between those variables and change of Bitcoin Closing Price. Before the regression analysis to confirm the relationship between change of Bitcoin Closing Price and the other variables, we performed the Unit-root test to verifying the stationary of time series data to avoid spurious regression. Then, using a stationary data, we did the regression analysis. As a result of the analysis, we found that the change of Bitcoin Closing Price has negative effects with search traffic of 'Bitcoin Ban' and US dollar index futures, while change of GPU vendors' stock price and change of WTI crude oil price showed positive effects. In case of 'Bitcoin Ban', it is directly determining the maintenance or abolition of Bitcoin trade, that's why consumer reacted sensitively and effected on change of Bitcoin Closing Price. GPU is raw material of Bitcoin mining. Generally, increasing of companies' stock price means the growth of the sales of those companies' products and services. GPU's demands increases are indirectly reflected to the GPU vendors' stock price. Making an interpretation, a rise in prices of GPU has put a crimp on the mining of Bitcoin. Consequently, GPU vendors' stock price effects on change of Bitcoin Closing Price. And we confirmed U.S. dollar index futures moved in the opposite direction with change of Bitcoin Closing Price. It moved like Gold. Gold was considered as a safe asset to consumers and it means consumer think that Bitcoin is a safe asset. On the other hand, WTI oil price went Bitcoin Closing Price's way. It implies that Bitcoin are regarded to investment asset like raw materials market's product. The variables that were not significant in the analysis were search traffic of bitcoin, search traffic of ransomware, search traffic of war, memory vendor's stock price, FOMC policy interest rates. In search traffic of bitcoin, we judged that interest in Bitcoin did not lead to purchase of Bitcoin. It means search traffic of Bitcoin didn't reflect all of Bitcoin's demand. So, it implies there are some factors that regulate and mediate the Bitcoin purchase. In search traffic of ransomware, it is hard to say concern of ransomware determined the whole Bitcoin demand. Because only a few people damaged by ransomware and the percentage of hackers requiring Bitcoins was low. Also, its information security problem is events not continuous issues. Search traffic of war was not significant. Like stock market, generally it has negative in relation to war, but exceptional case like Gulf war, it moves stakeholders' profits and environment. We think that this is the same case. In memory vendor stock price, this is because memory vendors' flagship products were not VRAM which is essential for Bitcoin supply. In FOMC policy interest rates, when the interest rate is low, the surplus capital is invested in securities such as stocks. But Bitcoin' price fluctuation was large so it is not recognized as an attractive commodity to the consumers. In addition, unlike the stock market, Bitcoin doesn't have any safety policy such as Circuit breakers and Sidecar. Through this study, we verified what factors effect on change of Bitcoin Closing Price, and interpreted why such change happened. In addition, establishing the characteristics of Bitcoin as a safe asset and investment asset, we provide a guide how consumer, financial institution and government organization approach to the cryptocurrency. Moreover, corroborating the factors affecting change of Bitcoin Closing Price, researcher will get some clue and qualification which factors have to be considered in hereafter cryptocurrency study.

• Asia Marketing Journal
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• v.14 no.1
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• pp.1-24
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• 2012

Nowadays, customer satisfaction has been one of company's major objectives, and the index to measure and communicate customer satisfaction has been generally accepted among business practices. The major issues of CSI(customer satisfaction index) are three questions, as follows: (a)what level of customer satisfaction is tolerable, (b)whether customer satisfaction and company performance has positive causality, and (c)what to do to improve customer satisfaction. Among these, the second issue is recently attracting academic research in several perspectives. On this study, the second issue will be addressed. Many researchers including Anderson have regarded customer satisfaction as core competencies, such as brand equity, customer equity. They want to verify following causality "customer satisfaction → market performance(market share, sales growth rate) → financial performance(operating margin, profitability) → corporate value performance(stock price, credit ratings)" based on the process model of marketing performance. On the other hand, Insoo Jeon and Aeju Jeong(2009) verified sequential causality based on the process model by the domestic data. According to the rejection of several hypotheses, they suggested the balance model of marketing performance as an alternative. The objective of this study, based on the existing process model, is to examine the causal relationship between customer satisfaction and corporate value performance. Anderson and Mansi(2009) proved the relationship between ACSI(American Customer Satisfaction Index) and credit ratings using 2,574 samples from 1994 to 2004 on the assumption that credit rating could be an indicator of a corporate value performance. The similar study(Sangwoon Yoon, 2010) was processed in Korean data, but it didn't confirm the relationship between KCSI(Korean CSI) and credit ratings, unlike the results of Anderson and Mansi(2009). The summary of these studies is in the Table 1. Two studies analyzing the relationship between customer satisfaction and credit ratings weren't consistent results. So, in this study we are to test the conflicting results of the relationship between customer satisfaction and credit ratings based on the research model considering Korean credit ratings. To prove the hypothesis, we suggest the research model as follows. Two important features of this model are the inclusion of important variables in the existing Korean credit rating system and government support. To control their influences on credit ratings, we included three important variables of Korean credit rating system and government support, in case of financial institutions including banks. ROA, ER, TA, these three variables are chosen among various kinds of financial indicators since they are the most frequent variables in many previous studies. The results of the research model are relatively favorable : R², F-value and p-value is .631, 233.15 and .000 respectively. Thus, the explanatory power of the research model as a whole is good and the model is statistically significant. The research model has good explanatory power, the regression coefficients of the KCSI is .096 as positive(+) and t-value and p-value is 2.220 and .0135 respectively. As a results, we can say the hypothesis is supported. Meanwhile, all other explanatory variables including ROA, ER, log(TA), GS_DV are identified as significant and each variables has a positive(+) relationship with CRS. In particular, the t-value of log(TA) is 23.557 and log(TA) as an explanatory variables of the corporate credit ratings shows very high level of statistical significance. Considering interrelationship between financial indicators such as ROA, ER which include total asset in their formula, we can expect multicollinearity problem. But indicators like VIF and tolerance limits that shows whether multicollinearity exists or not, say that there is no statistically significant multicollinearity in all the explanatory variables. KCSI, the main subject of this study, is a statistically significant level even though the standardized regression coefficients and t-value of KCSI is .055 and 2.220 respectively and a relatively low level among explanatory variables. Considering that we chose other explanatory variables based on the level of explanatory power out of many indicators in the previous studies, KCSI is validated as one of the most significant explanatory variables for credit rating score. And this result can provide new insights on the determinants of credit ratings. However, KCSI has relatively lower impact than main financial indicators like log(TA), ER. Therefore, KCSI is one of the determinants of credit ratings, but don't have an exceedingly significant influence. In addition, this study found that customer satisfaction had more meaningful impact on corporations of small asset size than those of big asset size, and on service companies than manufacturers. The findings of this study is consistent with Anderson and Mansi(2009), but different from Sangwoon Yoon(2010). Although research model of this study is a bit different from Anderson and Mansi(2009), we can conclude that customer satisfaction has a significant influence on company's credit ratings either Korea or the United State. In addition, this paper found that customer satisfaction had more meaningful impact on corporations of small asset size than those of big asset size and on service companies than manufacturers. Until now there are a few of researches about the relationship between customer satisfaction and various business performance, some of which were supported, some weren't. The contribution of this study is that credit rating is applied as a corporate value performance in addition to stock price. It is somewhat important, because credit ratings determine the cost of debt. But so far it doesn't get attention of marketing researches. Based on this study, we can say that customer satisfaction is partially related to all indicators of corporate business performances. Practical meanings for customer satisfaction department are that it needs to actively invest in the customer satisfaction, because active investment also contributes to higher credit ratings and other business performances. A suggestion for credit evaluators is that they need to design new credit rating model which reflect qualitative customer satisfaction as well as existing variables like ROA, ER, TA.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Journal of Korean Clinical Health Science
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• v.2 no.1
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• pp.1-16
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• 2014

Purpose. For a more efficient hospital management, this study aims to provide basic data so that the hospital management and staff in charge of hospital administration may systematically classify and collect hospital information, by analyzing the ordinary characters of an upper-level general hospital system and its common-type balance sheet, common-type profit and loss statement and financial ratio. Methods. By using information about an upper-level general hospital in C Province, provided by Alio(www.alio.go.kr), a public organization information provision site, Health Insurance Review & Assessment Service(www.hira.or.kr) and Ministry of Health and Welfare(www.mw.go.kr), this study analyzed 3 year's data from 2010 to 2012 and provided basic data by analyzing the ordinary characters of an upper-level general hospital system, and its common-type balance sheet, common-type profit and loss statement and financial ratio. Results. After analyzing the ordinary characters, common-type balance sheet, common-type proft and loss statement and financial ration of this general hospital, based on the 2010 to 2012 data, this study came to the following conclusions. Firstly, out of all the 1,069 hospital staff, there were 272 doctors working for 24 medical departments, out of whom the majority was 33 physicians. Most of the nurses were third-class ones, and about 2,000 outpatients and 600 inpatients on average were treated per day. Secondly, as a result of analyzing the common-type balance sheet, this study discovered that intangible assets out of fixed assets accounted for 41%, the majority, out of which usable and profitable donation asset buildings were of great importance, and the liquid assets increased more in 2012 than 2011. In the financial structure, the ratio of liquid liabilities was over 50% out of all the liabilities in 2012, and the ratio of purchase payables was high as well. The ratio of fixed liabilities reached up to 40%, out of which the retirement benefit appropriation fund was quite high. The capital was over 80%, but the surplus was in a deficit state. Compared to the capital, the ratio of total liabilities was about 90%, which indicates the financial structure of this general hospital was vulnerable. Thirdly, as a result of analyzing the common-type profit and loss statement, this study found out that the medical profits from inpatients were higher than profits from outpatients. The material cost was related to the medical quality of this general hospital, and it was as high as 30% out of the total costs and was about 45% of the labor cost. This general hospital showed 10% in the ratio of non-medical profits, and it seemed because of government subsidies. The ratios of medical profits and current net income were gradually changing for the better in 2012, compared to 2011. Lastly, as a result of analyzing the financial ratio, it was found that the liquidity ratio kept decreasing, from 110.7% in 2010 and 102.0% in 2011 to 77.2% in 2012. Besides, it was analyzed that the liquidity ratio and the net working capital ratio greatly decreased, while the quick ratio and the liquid ratio kept decreasing. Conclusions. 1. It is necessary to take the risk management into more consideration, and particularly, it is needed to differentiate and manage the levels of risk in detail. 2. By considering the fact that investments into hospital infrastructures were mostly based on liabilities, it is needed to deal with the scale of losses when evaluating risks. 3. By reflecting the character that investments into hospital infrastructures were based on liabilities, it is necessary to consider the ratio of ordinary profits as well as the ratio of operating profits to sales, and it is also important to consider sales productivity factors, such as the sales amount per a sickbed, by comparing them with other hospitals. As for limitations of this study, there may be some problems in terms of data interpretation because of the lack of information about the number of inpatients and the number of outpatients per year, which are needed for the break-even point analysis. Besides, to suggest a direction for the improvement of hospital management through analyses, non-financial factors should be reflected, such as the trend of economy, medical policies, and politic backgrounds. However, this study only focused on the common-type balance sheet, common-type profit and loss statement and financial ratio, so this study is actually limited to generalizing all the factors by analyzing public data only.