• Journal of Korea Society of Industrial Information Systems
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• v.28 no.1
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• pp.47-56
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• 2023

Mezzanine products are financial investment products with both bond and stock characteristics, which are mainly issued by low-grade companies in the financial market to secure liquidity. Therefore, bondholders investing in mezzanine products must make decisions about when they want to convert to stocks, along with whether they invest in mezzanine products issued by the company. Therefore, in this paper, a total of 2,000 learning data and 200 predictive experimental data with stock conversion events completed by major industries are divided, and mezzanine event algorithms are designed and performance analyzed through artificial neural network models. This topic is meaningful in that it proposed a methodology to scientifically solve the difficulties of exercising mezzanine products, which are of high interest in the financial field, by applying artificial neural network technology.

• The Journal of Korean Institute for Practical Engineering Education
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• v.3 no.2
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• pp.75-82
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• 2011

Recently the technology of green growth became more important role among the problems of running out of fossil fuels and global warming. To procure a new growth power combined with energy and green growth, a lot of investment for wind power, photovoltaics system, fuel cell and biofuel expanded day by day. Among these, wing power has a merit of a highly economic and no discharge of toxic substance. These days government and industrial companies actively support the development of wind power technology with lots of investment, but domestic related education and equipment still stay in research level when it is compared with foreign advanced countries which lead the wind power technology. Therefore to expand the base of basic skill required in the related industrials and to advance technology, we are in the situation to be needed a development of a new curriculum and educational equipment which is analogous with the actual industrial system. In this paper a development of a new educational equipment for the learning of turbine control is introduced. This educational equipment has been developed for students to get easy understanding for the theory of wind turbine control. And finally to demonstrate the effect of the use of the developed equipments and curriculum a questionnaire carried out.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.3
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• pp.29-40
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• 2020

Korean firms have been vigorously searching and exploring overseas market opportunities through export and overseas investment. As of end of 2019, there were more than 80,000 Korean overseas subsidiaries all over the world. With Korean overseas direct investment increasing recently, it became one of the important issues for overseas investors to be successful in the global market. There are a lot of studies on factors influencing the performance of overseas subsidiaries such as 'firm' and 'country' factors. This study empirically examines subsidiary performance determinants with 'industry architectures' by using a sample of 292 overseas Korean firm subsidiaries. Industry architectures are the stable but evolving sets of rules and roles through which labor is divided within a sector. This article considers how industry architectures shape success in international expansion. Industry architectures differ between countries, are not necessarily technologically determined, shape firms' capabilities and their competitive environment, and constitute a distinct level of analysis. We extract antecedents of related theory and empirically test its impact with a survey of Korean firms expanding in emerging economies. We would say this is the first study which tries to focus on industry architectures with the performance of Korean overseas subsidiaries. We find that separability and similarity of industry architectures across countries and localization of subsidiaries are robust and important predictors of success in international expansion. Our results suggest that industry architectures should be added to firm and country as an intermediate level of analysis that helps explain success in international expansion. While we established a pattern, much more remains to be done. We focus on the success of foreign operations, but we do not consider the broader benefits of going abroad, such as the learning or network effects that accrue at the level of the entire firm. The next obvious question is whether the results would differ in the developed market context. These we leave for future research to consider.

• Korea Trade Review
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• v.42 no.4
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• pp.159-184
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• 2017

OECD MNE Guidelines ('OECD Guidelines') was set forth in 1976 as a form of annex to the OECD Declaration on International Investment and Multinational Enterprises. The objective of the OECD Guidelines is to fulfill the implementation and adoption of the Responsible Business Conduct ('RBC') among the adhering states. To further the effectiveness of the OECD Guidelines, OECD, specifically the Investment Committee of OECD, has utilized National Contact Point ('NCP') structure. According to the Procedural Guidance annexed to the OECD Guidelines, peer learning is prescribed as an important tool for promoting and facilitating the implementation procedures of the OECD Guidelines. This paper, inter alia, is mainly focusing on the peer review mechanism applicable to NCPs because negative assessments by peers are likely to harm Korea's state image and entail international criticisms even though such reviews are conducted voluntarily. In addition, the Working Party on Responsible Business Conduct ('WPRBC') decided to have a peer review of Korean NCP in 2019. This paper first outlines the meaning and current applications of the peer review mechanism, and then analyzes specific peer review cases conducted in Denmark and Belgium in 2015, and in 2016, respectively. Lastly, based on the issues handled in the peer review reports on the above states, this paper makes a few recommendations for Korean government to prepare the peer review scheduled in 2019.

• Journal of the Korea Society of Computer and Information
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• v.17 no.10
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• pp.185-192
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• 2012

Call Center requires an ability of agents a lot more than face-to-face contact due to being achieved communication by non face-to face channel for contact with customers. In order to improve the ability of agents, Call Center carries out various educational training according to their work experience and function and with the accomplishment of educational training, Call Center is going to fulfill to develop its quality of counseling and productivity. On the other hand, due to investment of a lot of time and budget to educational training, it is needed to grasp and manage about its effectiveness that how helpful the training is for performance of work-site operations through evaluation of educational training. Having Seen researches about evaluation of educational training until these days, most researches have mainstream to measure satisfaction and a level of learning or degree that how the learning transfers to actions. It is found that a research about an entire evaluation model should be required. This study aims to investigate effectiveness of Call Center educational training from the level of recognition by reflecting Kirkpatrick's the four levels of learning evaluation. By the four levels, reaction, learning, behavior and results, the study found out a connection with standards of evaluation about each levels. In addition, by using structural equation modeling, it was examined goodness of fit about the entire model. Furthermore, by an alternative model, considering a direct relation between a factor of reaction and behavior, it was compared and examined goodness of fit of overall model of the study model and the alternative one.

• The Journal of Small Business Innovation
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• v.19 no.2
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• pp.53-68
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• 2016

Motivation and activities for technological learning, entrepreneurship, innovation, and creativity are driving forces of economic development in Asian countries. In the early stages of technological development, technological learning and entrepreneurship are efficient ways in which to catch up with advanced countries because firms can accumulate skills and knowledge quickly at relatively low risk. In the later stages of technological development, however, innovation and creativity become more important. This study aims to identify a) the factors (learning capabilities) that influence technological learning performance and b) barriers to enhancing innovation capabilities for the creative economy and organizations. The major part of this study is related to learning capabilities in the post-catch-up era. Based on a literature review and observations from Korean experiences, this study proposes a technological learning model composed of various influencing factors on technological learning. Three hypotheses are derived, and data are collected from Korean machine tool manufacturers. Intense interviews with CEOs and R&D directors are conducted using structured questionnaires. Statistical analysis, such as correlation and ANOVA are then carried out. Furthermore, this study addresses how to enhance innovation capabilities to move forward. Innovation enablers and barriers are identified by case studies and policy analysis. The results of the empirical study identify several levels of firms' learning capabilities and activities such as a) stock of technology, b) potential of technical labor, c) explicit technological efforts, d) readiness to learn, e) top management support, f) a formal technological learning system, g) high learning motivation, h) appropriate technology choice, and i) specific goal setting. These learning capabilities determine firms' learning performance, especially in the early stages of development. Furthermore, it is found that the critical factors for successful technological learning vary along the stages of technology development. Throughout the statistical and policy analyses, this study confirms that technological learning can be understood as an intrinsic principle of the technology development process. Firms perform proactive and creative learning in the late stages, while reactive and imitative learning prevails in the early stages. In addition, this study identifies the driving forces or facilitating factors enhancing innovation performance in the post catch-up era. The results of the preliminary case studies and policy analysis show some facilitating factors such as a) the strategic intent of the CEO and corporate culture, b) leadership and change agents, c) design principles and routines, d) ecosystem and collaboration with partners, and e) intensive R&D investment.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• Korean Journal of Comparative Education
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• v.27 no.1
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• pp.247-284
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• 2017

The purpose of this study is to analyze firm's motivation of participating and investing in apprenticeship in Germany and Korea, and to investigate institutional factors influencing firm's motivation. By comparing institutional factors of the two countries, it aims to drawing out policy implications for improving Korean apprenticeship. The main method for data collection was comprehensive literature review on international organizations, each countries' government and research institutes' policy materials, statistical data, research outputs and media resources related to each countries' apprenticeship. Considering whether firm's motivation for participating and investing in apprenticeship is production-oriented or investment-oriented, Germany is more inclined to investment motivation with firm's covering net cost during apprenticeship period. On the other hand, Korea is more inclined toward production orientation with firm's expectation of gaining net profit during the training period. Why is firm's training motivation different in these two countries? The author tried to find the reason from the difference of institutional factors of the countries by dividing institutional factors into 4 categories: context(tripartite relations, legal framework), input (flexibility of the system, government incentive), process(training contents, training duration, quality assurance), and output(completion/retention rate, apprentice's productivity). The key implication from the comparative analysis of institutional factors is that it is necessary to enforce companies to have "accountability" on the minimum critical elements, but also to ensure them to have "autonomy" on the rest of the elements.

• The Journal of the Korea Contents Association
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• v.22 no.3
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• pp.81-93
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• 2022

The stock investing is one of the most popular investment techniques. However, since it is not easy to obtain a return through actual investment, various strategies have been devised and tried in the past to obtain an effective and stable return. Among them, the volatility breakout strategy identifies a strong uptrend that exceeds a certain level on a daily basis as a breakout signal, follows the uptrend, and quickly earns daily returns. It is one of the popular investment strategies that are widely used to realize profits. However, it is difficult to predict stock prices by understanding the price trend pattern of stocks. In this paper, we propose a method of buying and selling stocks by predicting the return in trading based on the volatility breakout strategy using a bi-directional long short-term memory deep neural network that can realize a return in a short period of time. As a result of the experiment assuming actual trading on the test data with the learned model, it can be seen that the results outperform both the return and stability compared to the existing closing price prediction model using the long-short-term memory deep neural network model.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.