• The Journal of the Korea Contents Association
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• v.12 no.4
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• pp.401-406
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• 2012

Shanghai Stock Exchange is the largest stock exchange of emerging markets that there were listed firms 905, listed securities 1,537, listed stocks 949, total number of listed stocks 2 trillion 2000 billion shares. There is more development that is expected to occur in the future. The purpose of this study is to find determinants of capital structure to listed manufacturing firms in Shanghai Stock Exchange using multiple regression. Conclusions of this study are summarized as follows. First, firm size is positively related to debt ratio significantly at 1% significance level.. Second, the profitability is negatively related to debt ratio significantly at 1% significance level. Third, the growth ability is positively related to debt ratio significantly at 1% significance level. fourth, cash flow, the largest shares ownership, negotiable shares ratio are negatively related to debt ratio but they are not significant statistically. The result of this study provides information for investors and can be utilized to improvement of financial structure.

• Journal of the Korea Society of Computer and Information
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• v.27 no.1
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• pp.183-191
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• 2022

The Korea Stewardship Code 'Principles on the Fiduciary Responsibilities of Institutional Investors' was introduced in 2016 and the National Pension Service adopted it in 2018. the National Pension Service casted 'dessent' vote on the agenda which is able to reduce the ownership interest of shareholder in general meeting. This paper examines whether 'dissent' voting affected on the ownership interest of shareholder or not. The 'dissent' vote on the agenda are related to revision artical of corperation, appointment or compensation of director and auditor, approval of financial statements ect. The proxies of earnings management is discretionary accruals calculated by modified Jones model. The control variablies are size of assets, liabilities per assets, returns on assets. The results of this study are as followings. First, the 'dissent' voting on the agenda are related to revision artical of corperation, M&A, approval of financial statements ect. are not significant because their sample size is too small, Second, the 'dissent' voting on appointment of director and auditor affected on reduction of discretionary accruals. So the National Pension Service activism shall affect on increasing the ownership interest of shareholder. Third, the 'dissent' voting on compensation of director and auditor is not affected on reduction of discretionary accruals. This results show that 'unconditional dissent voting' on the agenda in general meeting is not to reduce the ownership interest of shareholder.

• Journal of Digital Convergence
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• v.17 no.7
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• pp.27-34
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• 2019

The IT industry has made rapid development and also had an economic impact on other industries Also, since the fourth industrial revolution has begun in recent years ago, so The convergence between IT and other industries is increasing. Therefore, the development of the IT industry will enhance the international competitiveness It will also have a major impact on the nation's economic growth. Therefore, IT firms should be more efficient in their production. so This paper analyzes the efficiency of High-growth IT firms using DEA model. We evaluate the CCR, BBC efficiency and RTS(return to scale) of 12 IT firms. As a result, there were 6 companies with BCC efficiency 1 and 4 companies with 1 CCR efficiency. The scale of profitability was analyzed by IRS as 7 companies and CRS as 5 companies. We also suggest the IT firms which can be benchmarked based on analyzed information. It is expected to provide investors and external stakeholders with very useful information on managerial management efficiency.

• The Korean Journal of Financial Management
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• v.8 no.1
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• pp.179-197
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• 1991

선도환의 가격을 결정하는 접근방법에는 2차자산(derivative assets)이라는 선도계약의 기본특성에 기초한 재정거래(arbitrage)에 의한 방법이 가장 많이 이용되고 있다. 재정거래방식에는 선도환과 현물외환가격간의 상호관련성에 의하여 선도환가격을 이자율평가설(covered interest rate parity : CIRP), 즉 현물가격과 양국간의 이자율차이의 합으로 표시하고 있다. 특히 현물가격과 이자율은 모두 현재시점에서 의사결정자에게 알려져 있기때문에 선도환가격은 확실성하에서 결정되어 미래에 대한 예측이나 투자자의 위험회피도와는 관계없이 결정된다는 것이 특징이다. 이자율평가설에 관한 많은 실증연구는 거래 비용을 고려한 경우 현실적으로 적절하다고 보고 있다(Frenkel and Levich ; 1975, 1977). 다른 방법으로는 선도환의 미래예측기능에만 촛점을 맞추어 가격결정을 하는 투기, 예측접근방법(speculative efficiency approach : 이하에서는 SEA라 함)이 있다. 이 방법 중에서 가장 단순한 형태로 표시된 가설, 즉 '선도환가격은 미래기대현물가격과 같다'는 가설은 대부분의 실증분석에서 기각되고 있다. 이에 따라 SEA에서는 선도환가격이 미래에 대한 기대치뿐만 아니라 위험프리미엄까지 함께 포함하고 있다는 새로운 가설을 설정하고 이에 대한 실증분석을 진행한다. 이 가설은 이론적 모형에서 출발한 것이 아니기 때문에, 특히 기대치와 위험프레미엄 모두가 측정 불가능하다는 점으로 인하여 실증분석상 많은 어려움을 겪게 된다. 이러한 어려움을 피하기 위하여 많은 연구에서는 이자율평가설을 이용하여 선도환가격에 포함된 위험프레미엄에 대해 추론 내지 그 행태를 설명하려고 한다. 이자율평가설을 이용하여 분석모형을 설정하고 실증분석을 하는 것은 몇가지 근본적인 문제점을 내포하고 있다. 먼저, 앞서 지적한 바와 같이 이자율평가설을 가정한다는 것은 SEA에서 주된 관심이 되는 미래예측이나 위험프레미엄과는 관계없이 선도가격이 결정 된다는 것을 의미한다. 따라서 이자율평가설을 가정하여 설정된 분석모형은 선도환시장의 효율성이나 균형가격결정에 대한 시사점을 제공할 수 없다는 것을 의미한다. 즉, 가정한 시장효율성을 실증분석을 통하여 다시 검증하려는 것과 같다. 이러한 개념적 차원에서의 문제점 이외에도 실증분석에서의 추정상의 문제점 또한 존재한다. 대부분의 연구들이 현물자산의 균형가격결정모형에 이자율평가설을 추가로 결합하기 때문에 이러한 방법으로 설정한 분석모형은 그 기초가 되는 현물가격모형과는 달리 자의적 조작이 가능한 형태로 나타나며 이를 이용한 모수의 추정은 불필요한 편기(bias)를 가지게 된다. 본 연구에서는 이러한 실증분석상의 편기에 관한 문제점이 명확하고 구체적으로 나타나는 Mark(1985)의 실증연구를 재분석하고 실증자료를 통하여 위험회피도의 추정치에 편기가 발생하는 근본원인이 이자율평가설을 부적절하게 사용하는데 있다는 것을 확인 하고자 한다. 실증분석결과는 본문의 <표 1>에 제시되어 있으며 그 내용을 간략하게 요약하면 다음과 같다. (A) 실증분석모형 : 본 연구에서는 다기간 자산가격결정모형중에서 대표적인 Lucas (1978)모형을 직접 사용한다. $$1={\beta}\;E_t[\frac{U'(C_{t+1})\;P_t\;s_{t+1}}{U'(C_t)\;P_{t+1}\;s_t}]$$ (2) $U'(c_t)$와 $P_t$는 t시점에서의 소비에 대한 한계효용과 소비재의 가격을, $s_t$와 $f_t$는 외환의 현물과 선도가격을, $E_t$와 ${\beta}$는 조건부 기대치와 시간할인계수를 나타낸다. Mark는 위의 식 (2)를 이자율평가설과 결합한 다음의 모형 (4)를 사용한다. $$0=E_t[\frac{U'(C_{t+1})\;P_t\;(s_{t+1}-f_t)}{U'(C_t)\;P_{t+1}\;s_t}]$$ (4) (B) 실증분석의 결과 위험회피계수 ${\gamma}$의 추정치 : Mark의 경우에는 ${\gamma}$의 추정치의 값이 0에서 50.38까지 매우 큰 폭의 변화를 보이고 있다. 특히 비내구성제품의 소비량과 선도프레미엄을 사용한 경우 ${\gamma}$의 추정치의 값은 17.51로 비정상적으로 높게 나타난다. 반면에 본 연구에서는 추정치가 1.3으로 주식시장자료를 사용한 다른 연구결과와 비슷한 수준이다. ${\gamma}$추정치의 정확도 : Mark에서는 추정치의 표준오차가 최소 15.65에서 최대 42.43으로 매우 높은 반면 본 연구에서는 0.3에서 0.5수준으로 상대적으로 매우 정확한 추정 결과를 보여주고 있다. 모형의 정확도 : 모형 (4)에 대한 적합도 검증은 시용된 도구변수(instrumental variables)의 종류에 따라 크게 차이가 난다. 시차변수(lagged variables)를 사용하지 않고 현재소비와 선도프레미엄만을 사용할 경우 모형 (4)는 2.8% 또는 2.3% 유의수준에서 기각되는 반면 모형 (2)는 5% 유의수준에서 기각되지 않는다. 위와같은 실증분석의 결과는 앞서 논의한 바와 같이 이자율평가설을 사용하여 균형자산가격 결정모형을 변형시킴으로써 불필요한 편기를 발생시킨다는 것을 명확하게 보여주는 것이다.

• Smart Media Journal
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• v.11 no.10
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• pp.9-21
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• 2022

The recent expansion of the use of social media has served as an opportunity to express users' opinions in real time in various fields such as society, economy, politics, and culture, and brought many platforms that provide various information about companies. Among them, Glassdoor.com which started 2008 in US provides users with evaluations of the current and the former employees of their companies and also provides a outlooks for the company's growth Such a platform has the utility of providing necessary information to whom want to find a job or change jobs. In addition to this, variable studies have shown that the company information provided through these platforms is useful for investors as well. In this study, it was tested whether the corporate growth prospects of employees provided by Jobplanet, a platform with a typical function similar to Glassdoor.com in Korea, have predictive power to predict actual corporate growth. The forecast provided by Jobplanet and the company's financial indicator data received from FnGuide were collected and composed of panel data and analyzed using fixed effect model regression analysis. As a result, it was found that companies with positive prospects had higher employment growth than companies with negative prospects. When the outlook was neutral, the employment growth rate was higher than that of companies with a negative outlook.

• The Korean Journal of Financial Management
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• v.24 no.4
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• pp.107-134
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• 2007

This paper tests the relationship among returns, volatilities, contracts and open interests of KOSPI 200 futures markets with the various dynamic models such as granger-causality, impulse response, variance decomposition and ARMA(1, 1)-GJR-GARCH(1, 1)-M. The sample period is from July 7, 1998 to December 29, 2005. The main empirical results are as follows; First, both contract change and open interest change of KOSPI 200 futures market tend to lead the returns of that according to the results of granger-causality, impulse response and variance decomposition with VAR. These results are likely to support the KOSPI 200 futures market seems to be inefficient with rejecting the hypothesis 1. Second, we also find that the returns and volatilities of the KOSPI 200 futures market are effected by both contract change and open interest change of that due to the results of ARMA(1,1)-GJR-GARCH(1,1)-M. These results also reject the hypothesis 1 and 2 suggesting the evidences of inefficiency of the KOSPI 200 futures market. Third, the study shows the asymmetric information effects among the variables. In addition, we can find the feedback relationship between the contract change and open interest change of KOSPI 200 futures market.

• Korean Journal of Construction Engineering and Management
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• v.13 no.4
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• pp.60-68
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• 2012

The past IMF foreign exchange crisis and subprime financial crisis had a big influence on variability of macroeconomics, even if the origin of its occurrence might be different. This not only had a significant infrequence on the overall industries, but also produced many insolvent companies by being closely linked with a management environment of an individual construction company leading the construction industry. Actually, the level of default risk of construction companies before and after fluctuation of macroeconomics gets to experience a rapid changing process, and a difference in reaction against shock exists according to each company. Accordingly, the purpose of this paper is to confirm the fluctuation process of the default risk of construction companies under the fluctuation of macroeconomics such as the IMF financial crisis and the subprime mortgage crisis. As an analysis result, it is judged that the subprime financial crisis gave bigger shock to construction companies than the foreign exchange crisis, and it is expected that this would have a relation with the construction market before shock of macroeconomics. In addition, it was analyzed that when comparing insolvent companies with normal companies, the recovery speed of normal companies is faster. It is judged that this was affected by a difference of internal business capacity between insolvent companies and normal companies.

• Journal of Digital Convergence
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• v.11 no.11
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• pp.191-196
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• 2013

The proportion of small and medium sized enterprises based on the number of corporations in China is 99%, the number of employees is 80%, and the proportion of GDP is 60%. These facts show that small and medium sized enterprises have an very significant effect on the economic growth of China. However, most of the researches conducted so far have studied large corporations, thus, there are relatively insufficient researches on the determinant of the capital structure of small and medium sized enterprises. Therefore, the purpose of this paper is to confirm the factors that determine the capital structure of small and medium sized enterprises in China. To achieve this purpose, we performed multiple regression method to 45 small and medium sized manufacturing enterprises listed on the Shenzhen Stock Exchange of China. Results of this study are as follows. First, the growth appeared to have a significant positive effect to the debt ratio in 1% significance level. Second, the profitability appeared to have a significant negative effect to debt ratio in 1% significance level. Third, the firm size appeared to have no effect on the debt ratio. Fourth, the secured value and tax shield effect by non-debt appeared to have a weak positive effect on the debt ratio, however these variables showed statistical insignificant.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.241-265
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• 2023

A corporate insolvency prediction model serves as a vital tool for objectively monitoring the financial condition of companies. It enables timely warnings, facilitates responsive actions, and supports the formulation of effective management strategies to mitigate bankruptcy risks and enhance performance. Investors and financial institutions utilize default prediction models to minimize financial losses. As the interest in utilizing artificial intelligence (AI) technology for corporate insolvency prediction grows, extensive research has been conducted in this domain. However, there is an increasing demand for explainable AI models in corporate insolvency prediction, emphasizing interpretability and reliability. The SHAP (SHapley Additive exPlanations) technique has gained significant popularity and has demonstrated strong performance in various applications. Nonetheless, it has limitations such as computational cost, processing time, and scalability concerns based on the number of variables. This study introduces a novel approach to variable selection that reduces the number of variables by averaging SHAP values from bootstrapped data subsets instead of using the entire dataset. This technique aims to improve computational efficiency while maintaining excellent predictive performance. To obtain classification results, we aim to train random forest, XGBoost, and C5.0 models using carefully selected variables with high interpretability. The classification accuracy of the ensemble model, generated through soft voting as the goal of high-performance model design, is compared with the individual models. The study leverages data from 1,698 Korean light industrial companies and employs bootstrapping to create distinct data groups. Logistic Regression is employed to calculate SHAP values for each data group, and their averages are computed to derive the final SHAP values. The proposed model enhances interpretability and aims to achieve superior predictive performance.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.59-77
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• 2012

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.