• The Korean Journal of Financial Management
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• v.26 no.3
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• pp.171-198
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• 2009

The primary purpose of this study is to examine the impact of new BIS regulation, which is the preparations to incorporate not only credit risk but also market and operation risk, on the bank behaviors. As methodology, SUR(seemingly unrelated regression) and pool unit test are used in the empirical analysis of banks survived in Korea. It is employed that quarterly data of BIS capital ratio, ratio of standard and below loans to total loans, ratio of liquid assets to liquid liabilities, allowances for credit losses, real GDP, yields of corporate bonds(3years, AA) covering the period of 2000Q1~2009Q1. As a result, it could be indicated that effectiveness and promoting improvements of BIS capital regulation policy as follows; First, it is explicitly seen that weight of lending had decreased and specific gravity of international investment had increased until before BIS regulation is built up a step for revised agreement in late 2001. Second, after more strengthening of BIS standard in late 2002, banks had a tendency to decrease the adjustment of assets weighted risk through issuing of national loan that is comparatively low profitability. Also, it is implicitly sought that BIS regulation is a bit of a factor to bring about credit crunch and then has become a bit of a factor of economic stagnation. Third, as the BIS regulation became hard, it let have a effort to raise the soundness of a credit loan because of selecting good debtor based on its credit ratings. Fourth, it should be arranged that the market disciplines, the effective superintendence system and the sound environment to be able to raise enormous bank capital easily, against the credit stringency and reinforce the soundness of banks etc. in Korea capital market.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.123-136
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• 2014

Recently, online shopping has further developed as the use of the Internet and a variety of smart mobile devices becomes more prevalent. The increase in the scale of such shopping has led to the creation of many Internet shopping malls. Consequently, there is a tendency for increasingly fierce competition among online retailers, and as a result, many Internet shopping malls are making significant attempts to attract online users to their sites. One such attempt is keyword marketing, whereby a retail site pays a fee to expose its link to potential customers when they insert a specific keyword on an Internet portal site. The price related to each keyword is generally estimated by the keyword's frequency of appearance. However, it is widely accepted that the price of keywords cannot be based solely on their frequency because many keywords may appear frequently but have little relationship to shopping. This implies that it is unreasonable for an online shopping mall to spend a great deal on some keywords simply because people frequently use them. Therefore, from the perspective of shopping malls, a specialized process is required to extract meaningful keywords. Further, the demand for automating this extraction process is increasing because of the drive to improve online sales performance. In this study, we propose a methodology that can automatically extract only shopping-related keywords from the entire set of search keywords used on portal sites. We define a shopping-related keyword as a keyword that is used directly before shopping behaviors. In other words, only search keywords that direct the search results page to shopping-related pages are extracted from among the entire set of search keywords. A comparison is then made between the extracted keywords' rankings and the rankings of the entire set of search keywords. Two types of data are used in our study's experiment: web browsing history from July 1, 2012 to June 30, 2013, and site information. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The original sample dataset contains 150 million transaction logs. First, portal sites are selected, and search keywords in those sites are extracted. Search keywords can be easily extracted by simple parsing. The extracted keywords are ranked according to their frequency. The experiment uses approximately 3.9 million search results from Korea's largest search portal site. As a result, a total of 344,822 search keywords were extracted. Next, by using web browsing history and site information, the shopping-related keywords were taken from the entire set of search keywords. As a result, we obtained 4,709 shopping-related keywords. For performance evaluation, we compared the hit ratios of all the search keywords with the shopping-related keywords. To achieve this, we extracted 80,298 search keywords from several Internet shopping malls and then chose the top 1,000 keywords as a set of true shopping keywords. We measured precision, recall, and F-scores of the entire amount of keywords and the shopping-related keywords. The F-Score was formulated by calculating the harmonic mean of precision and recall. The precision, recall, and F-score of shopping-related keywords derived by the proposed methodology were revealed to be higher than those of the entire number of keywords. This study proposes a scheme that is able to obtain shopping-related keywords in a relatively simple manner. We could easily extract shopping-related keywords simply by examining transactions whose next visit is a shopping mall. The resultant shopping-related keyword set is expected to be a useful asset for many shopping malls that participate in keyword marketing. Moreover, the proposed methodology can be easily applied to the construction of special area-related keywords as well as shopping-related ones.

• Journal of Service Research and Studies
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• v.9 no.3
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• pp.39-60
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• 2019

The purpose of this study is to analyze the determinants of insurance product cross selling performance. For the study, 11 insurance managers and 2 sales managers belonging to A insurance agency were selected and in-depth interviews were conducted. The analysis of the research data was done by the open coding method suggested by Strauss & Corbin(2001). As a result, 84 concepts, 28 subcategories and 10 categories were derived. The ten categories that were determinants of insurance product cross-selling performance were personal characteristics, consultation method, cross-selling ratio, sales culture, education, customer change, customer DB provision, satisfaction, business support system, and customer service. In order to verify the qualitative results, quantitative analysis was emplyed to the actual performance data of insurance planners belonging to A insurance agency during April 2016~March 2019. As a result of the analysis, the age, position, and the number of months worked in the insurance company had a statistically significant effect on the number of life insurance contracts in total insurance contracts and life insurance contracts in total insurance contracts. In addition, the age, position, and the number of months worked in the insurance company had a statistically significant negative impact on the number of non-life insurance contracts in the total number of insurance contracts and the total amount of insurance contracts in total insurance contracts. The result of this study can be an important basic data for the development of educational programs and job support systems for the training of insurance planners. Insurance companies should refer to ten categories derived from qualitative research in order to increase the performance of insurance planners and to promote long-term service. Especially, it is necessary to develop specialized education programs and job support systems so that cross sales that increase the proportion of life insurance sales increase.

• Korean Journal of Heritage: History & Science
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• v.53 no.2
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• pp.254-269
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• 2020

The purpose of this study is to propose guidance for wall renewal that is appropriate for an ancient city wall through application of advanced research and theories in wall design. It is a streetscape improvement project which forms part of the "Ancient City Image Finding Project". Study methods consist of advanced research classification, wall design theory contemplation, and analysis of the significance of designated ancient city areas and the "Ancient City Image Finding Project" status. Based on these methods, case study candidates were selected, case status and problems were identified, and improvement proposals were analyzed by comparing various features. Advanced wall research was classified into six categories including analysis of wall characteristics; wall design principle applications; wall structure, color, shape, and application; modern reinterpretation; palace walls; and house, temple, and village walls. The wall is an element of the streetscape improvement component of the "Ancient City Image Finding Project", with the characteristic of providing preceding experience in visual and cognitive awareness than interior structure. Case candidates for ancient city wall improvement are based on the composition distribution of the special conservation district in each ancient city as well as the conservation promotion district. Ultimately, the surrounding village of Gongju-si Geumseong-Dong Songsanri-gil, adjacent to the Royal Tomb of King Muryeong, was selected as the candidate. The "Ancient City Image Finding Project" of the surrounding village of Gongju-si Geumseong-Dong Songsanri-gil began with new Hanok construction. However, wall maintenance did not begin concurrently with that new Hanok construction. Support and maintenance took place afterwards as an exterior maintenance project for roadside structures. If the Hanok and wall were evaluated and constructed at the same time, the wall would have been built in unison with the size and design of the Hanok. The layout of the main building and wall of the Hanok is deemed to be a structure that is closed tightly because of its spatial proximity and tall height. Songsan-ri-gil's wall design should create a calm, subtle, and peaceful atmosphere with shapes, colors, and materials that express ancient city characteristics, but it is in an awkward position due to its sharpness and narrowness. The cause of the problem at Gongju-si Geumseong-dong Songsanri-gil, the case candidate, is that it is lacking significantly in terms of the aesthetic factors that traditional walls should possess. First, aesthetic consciousness seems to have disappeared during the selection and application process of the wall's natural materials. Second, the level of completion in design and harmony is absent. Maintenance guidance after analyzing the cause of problems in ancient city wall maintenance at Gongju-si Geumseong-dong Songsanri-gil, the subject area of research, is as follows: First, the Hanok design and layout of the wall and main gate should be reviewed simultaneously. Second, the one-sided use of natural stone wall in the Hanok wall design should be reexamined. Third, a permanent system to coordinate the opinions of citizens and experts during the planning and design phases should be employed. Fourth and finally, the Hanok's individuality shall be collectivized and its value as a cultural asset representing the identity of the community shall be increased.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.1
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• pp.139-149
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• 2019

The purpose of this study is to contribute to the minimization of the social cost due to the insolvency by improving the success rate of the startups by providing useful information to the founders and the start-up support institutions through analysis of non-financial information affecting the failure of the startups. This study is aimed at entrepreneurs. The entrepreneurs that are defined by the credit guarantee institutions generally refer to entrepreneurs within 5 years of establishment. The data used in the study are sampled from the companies that were supported by the start-up guarantee from January 2014 to December 2013 as the end of December 2017. The total number of sampled firms is 2,826, 2,267 companies (80.2%), and 559 non-performing companies (19.8%). The non-financial information of the entrepreneur was divided into the entrepreneur characteristics information, the entrepreneur characteristics information, the entrepreneur asset information and the entrepreneur 's credit information, and cross-tabulations and logistic regression analysis were conducted. As a result of cross-tabulations, univariate analysis showed that personal credit rating, presence in the industry, presence of residential housing, presence of employees, and presence of financial statements were selected as significant variables. As a result of the logistic regression analysis, three variables such as personal credit rating, occupation in the industry, and presence of residential house were found to be important factors affecting the failure of founding companies. This result shows the importance of entrepreneur 's personal credibility and experience and entrepreneur' s assets in business management. The start-up support institutions should reflect these results in the entrepreneur 's credit evaluation system, and the entrepreneurs need training on the importance of the personal credit and the management plan in the entrepreneurial education. The results of this analysis will contribute to the minimization of the incapacity of startups by providing useful non-financial information to founders and start-up support organizations.

• Journal of the Korean Institute of Landscape Architecture
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• v.47 no.3
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• pp.1-11
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• 2019

The purpose of this study is to propose a sustainable village-unit urban regeneration plan for the Ewha Mural Village, where mural artists recovered concrete fences to be followed by some residents damaging the mural paintings. Through a review of the existing literature and a preliminary survey, we derived the urban regeneration factors (environmental sustainability, economic sustainability, and social sustainability) applicable at the village level. After an empirical survey on the residents, we tried to identify various problems of the Ewha Mural Village. Residents selected the factors of accessibility, parking management, diversity of industries, creation of new jobs, community participation of residents for the mural village's activation, and stable living spaces. In the case of Ewha Mural Village, physical environment factors for the residents at the time of construction were not considered and the village was mainly planned using budget-based murals. Since then, the inequality of economic benefits intensified the conflicts among the residents. In addition, public benefits, such as establishing new industries and employing outsiders, were not provided, and these facts appear to have led to an unsustainable murals village, in which the murals that are the protagonists of the village revitalization are being destroyed. Therefore, the urban regeneration of Ewha Mural Village should be designed considering a region where some residential areas can be transformed into tourist areas. In addition, it is essential to employ a win-win method to improve the living environment, such as road maintenance, not only partial economic benefits, such as increased land-value, and to increase resident's value as a common asset within the village itself.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.175-181
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• 2024

The U.N. Security Council's North Korea Sanctions Committee estimated that the amount of North Korea's cyberattacks on virtual asset-related companies from 2017 to 2023 was about 4 trillion won. North Korea's cyberattacks have secured funds through cryptocurrency hacking as it has been restricted from securing foreign currency due to economic sanctions by the international community, and it also shows the form of technology theft against defense companies, and illegal assets are being used to maintain the Kim Jong-un regime and develop nuclear and missile development. When North Korea conducted its sixth nuclear test on September 3, 2017, and declared the completion of its national nuclear armament following the launch of an intercontinental ballistic missile on November 29 of the same year, the U.N. imposed sanctions on North Korea, which are considered the strongest economic sanctions in history. In these difficult economic situations, North Korea tried to overcome the crisis through cyberattacks, but as a result of analyzing the changes through the North's cyber attack cases, the strategic goal from the first period from 2009 to 2016 was to verify and show off North Korea's cyber capabilities through the neutralization of the national network and the takeover of information, and was seen as an intention to create social chaos in South Korea. When foreign currency earnings were limited due to sanctions against North Korea in 2016, the second stage seized virtual currency and secured funds to maintain the Kim Jong-un regime and advance nuclear and missile development. The third stage is a technology hacking of domestic and foreign defense companies, focusing on taking over key technologies to achieve the five strategic weapons tasks proposed by Chairman Kim Jong-un at the 8th Party Congress in 2021. At the national level, security measures for private companies as well as state agencies should be established against North Korea's cyberattacks, and measures for legal systems, technical problems, and budgets related to science are urgently needed. It is also necessary to establish a system and manpower to respond to the ever-developing cyberattacks by focusing on cultivating and securing professional manpower such as white hackers.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.147-168
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• 2017

There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.

• Strategy21
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• s.46
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• pp.239-276
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• 2020

The failure of early economic sanctions aimed at hurting the overall economies of targeted states called for a more sophisticated design of economic sanctions. This paved way for the advent of 'smart sanctions,' which target the supporters of the regime instead of the public mass. Despite controversies over the effectiveness of economic sanctions as a coercive tool to change the behavior of a targeted state, the transformation from 'comprehensive sanctions' to 'smart sanctions' is gaining the status of a legitimate method to impose punishment on states that do not conform to international norms, the nonproliferation of weapons of mass destruction in this particular context of the paper. The five permanent members of the United Nations Security Council proved that it can come to an accord on imposing economic sanctions over adopting resolutions on waging military war with targeted states. The North Korean nuclear issue has been the biggest security threat to countries in the region, even for China out of fear that further developments of nuclear weapons in North Korea might lead to a 'domino-effect,' leading to nuclear proliferation in the Northeast Asia region. Economic sanctions had been adopted by the UNSC as early as 2006 after the first North Korean nuclear test and has continually strengthened sanctions measures at each stage of North Korean weapons development. While dubious of the effectiveness of early sanctions on North Korea, recent sanctions that limit North Korea's exports of coal and imports of oil seem to have an impact on the regime, inducing Kim Jong-un to commit to peaceful talks since 2018. The purpose of this paper is to add a variable to the factors determining the success of economic sanctions on North Korea: preventing North Korea's evasion efforts by conducting illegal transshipments at sea. I first analyze the cause of recent success in the economic sanctions that led Kim Jong-un to engage in talks and add the maritime element to the argument. There are three conditions for the success of the sanctions regime, and they are: (1) smart sanctions, targeting commodities and support groups (elites) vital to regime survival., (2) China's faithful participation in the sanctions regime, and finally, (3) preventing North Korea's maritime evasion efforts.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.171-191
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• 2020

This study focused on digital music, which is the most valuable cultural asset in the modern society and occupies a particularly important position in the flow of the Korean Wave. Digital music was collected based on the "Gaon Chart," a well-established music chart in Korea. Through this, the changes in the ranking of the music that entered the chart for 73 weeks were collected. Afterwards, patterns with similar characteristics were derived through time series cluster analysis. Then, a descriptive analysis was performed on the notable features of each pattern. The research process suggested by this study is as follows. First, in the data collection process, time series data was collected to check the ranking change of digital music. Subsequently, in the data processing stage, the collected data was matched with the rankings over time, and the music title and artist name were processed. Each analysis is then sequentially performed in two stages consisting of exploratory analysis and explanatory analysis. First, the data collection period was limited to the period before 'the music bulk buying phenomenon', a reliability issue related to music ranking in Korea. Specifically, it is 73 weeks starting from December 31, 2017 to January 06, 2018 as the first week, and from May 19, 2019 to May 25, 2019. And the analysis targets were limited to digital music released in Korea. In particular, digital music was collected based on the "Gaon Chart", a well-known music chart in Korea. Unlike private music charts that are being serviced in Korea, Gaon Charts are charts approved by government agencies and have basic reliability. Therefore, it can be considered that it has more public confidence than the ranking information provided by other services. The contents of the collected data are as follows. Data on the period and ranking, the name of the music, the name of the artist, the name of the album, the Gaon index, the production company, and the distribution company were collected for the music that entered the top 100 on the music chart within the collection period. Through data collection, 7,300 music, which were included in the top 100 on the music chart, were identified for a total of 73 weeks. On the other hand, in the case of digital music, since the cases included in the music chart for more than two weeks are frequent, the duplication of music is removed through the pre-processing process. For duplicate music, the number and location of the duplicated music were checked through the duplicate check function, and then deleted to form data for analysis. Through this, a list of 742 unique music for analysis among the 7,300-music data in advance was secured. A total of 742 songs were secured through previous data collection and pre-processing. In addition, a total of 16 patterns were derived through time series cluster analysis on the ranking change. Based on the patterns derived after that, two representative patterns were identified: 'Steady Seller' and 'One-Hit Wonder'. Furthermore, the two patterns were subdivided into five patterns in consideration of the survival period of the music and the music ranking. The important characteristics of each pattern are as follows. First, the artist's superstar effect and bandwagon effect were strong in the one-hit wonder-type pattern. Therefore, when consumers choose a digital music, they are strongly influenced by the superstar effect and the bandwagon effect. Second, through the Steady Seller pattern, we confirmed the music that have been chosen by consumers for a very long time. In addition, we checked the patterns of the most selected music through consumer needs. Contrary to popular belief, the steady seller: mid-term pattern, not the one-hit wonder pattern, received the most choices from consumers. Particularly noteworthy is that the 'Climbing the Chart' phenomenon, which is contrary to the existing pattern, was confirmed through the steady-seller pattern. This study focuses on the change in the ranking of music over time, a field that has been relatively alienated centering on digital music. In addition, a new approach to music research was attempted by subdividing the pattern of ranking change rather than predicting the success and ranking of music.