• 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.

• Journal of Korean Society of Forest Science
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• v.102 no.4
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• pp.506-514
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• 2013

This study examined the feasibility on the construction of a wood industrialization service center for a wood industry cluster establishment in Jeollanam-do. Construction of the wood industrialization service center is based on a discount rate of 3.5%, an investment period of 4 years, a business operations period of 16 years and an investment cost of 24600 million won; the total amount of the net present value, the cost-benefit ratio and the internal rate of return were assumed to be 2.579 million won, 2.51%, and 10.1%, respectively. In addition, the production inducement coefficient, the induced production effect, the income-induced coefficient, the income inducement effect, the employment inducement coefficient, and the employment inducement effect were estimated 1.4345, 35287 million won, 0.1655, 4000.7 million won, and 0.4665, 1,145 people, in the effects of the wood related industries using the multi-regional input-output model, respectively. Financial independence of operating income to cover its own costs incurred in accordance with the operating project might be practicable.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.287-316
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• 2023

As global energy crisis and the COVID-19 pandemic have emerged as social issues, there is a growing demand for companies to move away from profit-centric business models and embrace sustainable management that balances environmental, social, and governance (ESG) factors. ESG activities of companies vary across industries, and industry-specific weights are applied in ESG evaluations. Therefore, it is important to develop strategic management approaches that reflect the characteristics of each industry and the importance of each ESG factor. Additionally, with the stance of strengthened focus on ESG disclosures, specific guidelines are needed to identify and report on sustainable management activities of domestic companies. To understand corporate sustainability strategies, analyzing ESG reports and news articles by industry can help identify strategic characteristics in specific industries. However, each company has its own unique strategies and report structures, making it difficult to grasp detailed trends or action items. In our study, we analyzed ESG reports (2019-2021) and news articles (2019-2022) of six companies in the 'Finance,' 'Manufacturing,' and 'IT' sectors to examine the sustainability strategies of leading domestic ESG companies. Text mining techniques such as keyword frequency analysis and topic modeling were applied to identify industry-specific, ESG element-specific management strategies and issues. The analysis revealed that in the 'Finance' sector, customer-centric management strategies and efforts to promote an inclusive culture within and outside the company were prominent. Strategies addressing climate change, such as carbon neutrality and expanding green finance, were also emphasized. In the 'Manufacturing' sector, the focus was on creating sustainable communities through occupational health and safety issues, sustainable supply chain management, low-carbon technology development, and eco-friendly investments to achieve carbon neutrality. In the 'IT' sector, there was a tendency to focus on technological innovation and digital responsibility to enhance social value through technology. Furthermore, the key issues identified in the ESG factors were as follows: under the 'Environmental' element, issues such as greenhouse gas and carbon emission management, industry-specific eco-friendly activities, and green partnerships were identified. Under the 'Social' element, key issues included social contribution activities through stakeholder engagement, supporting the growth and coexistence of members and partner companies, and enhancing customer value through stable service provision. Under the 'Governance' element, key issues were identified as strengthening board independence through the appointment of outside directors, risk management and communication for sustainable growth, and establishing transparent governance structures. The exploration of the relationship between ESG disclosures in reports and ESG issues in news articles revealed that the sustainability strategies disclosed in reports were aligned with the issues related to ESG disclosed in news articles. However, there was a tendency to strengthen ESG activities for prevention and improvement after negative media coverage that could have a negative impact on corporate image. Additionally, environmental issues were mentioned more frequently in news articles compared to ESG reports, with environmental-related keywords being emphasized in the 'Finance' sector in the reports. Thus, ESG reports and news articles shared some similarities in content due to the sharing of information sources. However, the impact of media coverage influenced the emphasis on specific sustainability strategies, and the extent of mentioning environmental issues varied across documents. Based on our study, the following contributions were derived. From a practical perspective, companies need to consider their characteristics and establish sustainability strategies that align with their capabilities and situations. From an academic perspective, unlike previous studies on ESG strategies, we present a subdivided methodology through analysis considering the industry-specific characteristics of companies.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.155-170
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• 2018

Predicting a company's financial bankruptcy is traditionally one of the most crucial forecasting problems in business analytics. In previous studies, prediction models have been proposed by applying or combining statistical and machine learning-based techniques. In this paper, we propose a novel intelligent prediction model based on the simulated annealing which is one of the well-known optimization techniques. The simulated annealing is known to have comparable optimization performance to the genetic algorithms. Nevertheless, since there has been little research on the prediction and classification of business decision-making problems using the simulated annealing, it is meaningful to confirm the usefulness of the proposed model in business analytics. In this study, we use the combined model of simulated annealing and machine learning to select the input features of the bankruptcy prediction model. Typical types of combining optimization and machine learning techniques are feature selection, feature weighting, and instance selection. This study proposes a combining model for feature selection, which has been studied the most. In order to confirm the superiority of the proposed model in this study, we apply the real-world financial data of the Korean companies and analyze the results. The results show that the predictive accuracy of the proposed model is better than that of the naïve model. Notably, the performance is significantly improved as compared with the traditional decision tree, random forests, artificial neural network, SVM, and logistic regression analysis.

• Journal of Nutrition and Health
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• v.48 no.6
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• pp.542-557
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• 2015

Purpose: The aim of this study is to develop an evaluation tool for operation of food safety and nutrition education projects for middle class elderly using the concept of the balanced score card. Methods: After the draft of the evaluation tool for the elderly training projects was completed, it was revised into the questionnaire and the validity of the indicators was tested by the Delphi group. The validity of the indicators was rated using a 5-point scale. The Delphi group consisted of 26 experts in the education sector, 16 government officials, and 24 professionals of the related area in communities. The first round test was conducted from July 9 to July 17, 2012, and 45 persons responded. The second round test was conducted from July 18 to July 25 and 32 persons responded. Results: The indicators, which were answered by more than 75 percent of the experts as 'agree' (4 points), 'strongly agree' (5 point) were included as the final indicators for the evaluation tool: 28 items out of 36 in outcome perspectives, 9 items out of 12 in process perspectives, and 17 out of 20 items in structure perspectives. The score was allocated as 50 points for outcome indicators, 20 points for process indicators, and 30 points for structure indicators. Conclusion: Completion of the evaluation tool is a prerequisite to determine whether the program is effectively implemented. The monitoring tool developed in the study could be applied for identification of the most optimal delivery path for the food safety and nutrition education program, for the spread of the food safety and nutrition education program for middle class elderly.

• Korean Security Journal
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• no.30
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• pp.85-115
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• 2012

The private security industry has expanded in proportion to economic developments throughout the world, largely because the existing security services provided by the government do not satisfy demands of various economic entities and people in the society for better security service. Therefore, it would not be unfair to say that security services by private sectors are decided by its quality, price, and customers' needs. A refined management system, however, is essential to assess qualification of security companies and their service quality, given the nature of public goods of security service. Despite the steady growth of private security industry in Korea, however, it has been continuously criticized that its security management system for better qualification of security guards, training, and private security companies have not been fully updated enough to guarantee good quality. This paper aims to gain insights to effective policy formation in the Korean private security industry, through reviewing the licensing system of private security guards and the Approved Contractor Scheme (hereinafter the ACS) in the UK- that has on one hand systematically regulated private security industry, but on other hand has enforced public-private cooperation by laying significant stress on autonomy of private security companies. The distinctive characteristic of the UK policy for the private security is that the Security Industry Authority (hereinafter the SIA), an independent authority, is leading development of the private security industry of the UK through specialized private security regulation and enhanced service quality. In addition, the UK is developing quality of security service with transparent financial management and recruitment of good quality security guards by adopting not only substantially specified regulations and standards, but the voluntary ACS system. Moreover, the SIA analyzes customers' demands for security service specializing the policy for private security through conducting a variety of surveys. With the analysis of the UK private security system, this paper suggests that the Korean government change from a non-specialized private security regulation system by the National Police Agency to an independently specialized private security authority like the SIA and adopt the compulsory licensing and the ACS system of the UK.

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

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

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.8 no.3
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• pp.113-124
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• 2013

The government strives to create venture ecosystem for realizing creative economy, at the same time, the Mistry of Education is spending huge resources and efforts to spread entrepreneurship education to universities in Korea. Since entrepreneurship education gives motivation to individuals and creates market innovation and these connect to the growth of national economy through increased efficiency, entrepreneurship education is becoming increasingly more important for realizing creative economy. Based on the importance, entrepreneurship education in the universities is now spreading rapidly. However, college students' entrepreneurial intention has still not been improved comparing to spreading entrepreneurship education. To overcome the poor improvement, entrepreneurship education needs to be driven more systematic direction through the study on the effect of students' motivation and environment. In this study, entrepreneurship as a part of careers perspectives, is analyzed on students' career reasons with entrepreneurial intention. For this study, 918 surveys was collected from 7 universities having entrepreneurship courses in Seoul and Gyeonggi regions in 2012 and analyzed 858 surveys in order to prove the hypothesis. The results disclosed the relationship between students' career reasons and entrepreneurial self-efficacy and intention. Motivation factors of self-realization, innovation and role model have positive effect on entrepreneurial self-efficacy following by increased entrepreneurial intention, unlike the common notion financial success and independence factors are not significant with entrepreneurial intention of students. Based on these results having meaningful implication to Korea entrepreneurship education, this study is expected to have contribution to the successful promoting the creative economy realization of our government.

• Journal of Korean Society of Transportation
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• v.35 no.4
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• pp.360-373
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• 2017

The BTO-a projects is the types, which has a demand risk among the type of PPP projects in Korea. When demand risk is realized, private investor encounters financial difficulties due to lower revenue than its expectation and the government may also have a problem in stable infrastructure operation. In this regards, the government has applied various risk sharing policies in response to demand risk. However, the amount of government's risk sharing is the government's contingent liabilities as a result of demand uncertainty, and it fails to be quantified by the conventional NPV method of expressing in the text of the concession agreement. The purpose of this study is to estimate the value of investment risk sharing by the government considering the demand risk in the profit sharing system (BTO-a) introduced in 2015 as one of the demand risk sharing policy. The investment risk sharing will take the form of options in finance. Private investors have the right to claim subsidies from the government when their revenue declines, while the government has the obligation to pay subsidies under certain conditions. In this study, we have established a methodology for estimating the value of investment risk sharing by using the Black - Scholes option pricing model and examined the appropriateness of the results through case studies. As a result of the analysis, the value of investment risk sharing is estimated to be 12 billion won, which is about 4% of the investment cost of the private investment. In other words, it can be seen that the government will invest 12 billion won in financial support by sharing the investment risk. The option value when assuming the traffic volume risk as a random variable from the case studies is derived as an average of 12.2 billion won and a standard deviation of 3.67 billion won. As a result of the cumulative distribution, the option value of the 90% probability interval will be determined within the range of 6.9 to 18.8 billion won. The method proposed in this study is expected to help government and private investors understand the better risk analysis and economic value of better for investment risk sharing under the uncertainty of future demand.