• Asia pacific journal of information systems
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• v.19 no.2
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• pp.139-155
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• 2009

For the last few decades, many studies have tried to explore and unveil venture companies' success factors and unique features in order to identify the sources of such companies' competitive advantages over their rivals. Such venture companies have shown tendency to give high returns for investors generally making the best use of information technology. For this reason, many venture companies are keen on attracting avid investors' attention. Investors generally make their investment decisions by carefully examining the evaluation criteria of the alternatives. To them, credit rating information provided by international rating agencies, such as Standard and Poor's, Moody's and Fitch is crucial source as to such pivotal concerns as companies stability, growth, and risk status. But these types of information are generated only for the companies issuing corporate bonds, not venture companies. Therefore, this study proposes a method for evaluating venture businesses by presenting our recent empirical results using financial data of Korean venture companies listed on KOSDAQ in Korea exchange. In addition, this paper used multi-class SVM for the prediction of DEA-based efficiency rating for venture businesses, which was derived from our proposed method. Our approach sheds light on ways to locate efficient companies generating high level of profits. Above all, in determining effective ways to evaluate a venture firm's efficiency, it is important to understand the major contributing factors of such efficiency. Therefore, this paper is constructed on the basis of following two ideas to classify which companies are more efficient venture companies: i) making DEA based multi-class rating for sample companies and ii) developing multi-class SVM-based efficiency prediction model for classifying all companies. First, the Data Envelopment Analysis(DEA) is a non-parametric multiple input-output efficiency technique that measures the relative efficiency of decision making units(DMUs) using a linear programming based model. It is non-parametric because it requires no assumption on the shape or parameters of the underlying production function. DEA has been already widely applied for evaluating the relative efficiency of DMUs. Recently, a number of DEA based studies have evaluated the efficiency of various types of companies, such as internet companies and venture companies. It has been also applied to corporate credit ratings. In this study we utilized DEA for sorting venture companies by efficiency based ratings. The Support Vector Machine(SVM), on the other hand, is a popular technique for solving data classification problems. In this paper, we employed SVM to classify the efficiency ratings in IT venture companies according to the results of DEA. The SVM method was first developed by Vapnik (1995). As one of many machine learning techniques, SVM is based on a statistical theory. Thus far, the method has shown good performances especially in generalizing capacity in classification tasks, resulting in numerous applications in many areas of business, SVM is basically the algorithm that finds the maximum margin hyperplane, which is the maximum separation between classes. According to this method, support vectors are the closest to the maximum margin hyperplane. If it is impossible to classify, we can use the kernel function. In the case of nonlinear class boundaries, we can transform the inputs into a high-dimensional feature space, This is the original input space and is mapped into a high-dimensional dot-product space. Many studies applied SVM to the prediction of bankruptcy, the forecast a financial time series, and the problem of estimating credit rating, In this study we employed SVM for developing data mining-based efficiency prediction model. We used the Gaussian radial function as a kernel function of SVM. In multi-class SVM, we adopted one-against-one approach between binary classification method and two all-together methods, proposed by Weston and Watkins(1999) and Crammer and Singer(2000), respectively. In this research, we used corporate information of 154 companies listed on KOSDAQ market in Korea exchange. We obtained companies' financial information of 2005 from the KIS(Korea Information Service, Inc.). Using this data, we made multi-class rating with DEA efficiency and built multi-class prediction model based data mining. Among three manners of multi-classification, the hit ratio of the Weston and Watkins method is the best in the test data set. In multi classification problems as efficiency ratings of venture business, it is very useful for investors to know the class with errors, one class difference, when it is difficult to find out the accurate class in the actual market. So we presented accuracy results within 1-class errors, and the Weston and Watkins method showed 85.7% accuracy in our test samples. We conclude that the DEA based multi-class approach in venture business generates more information than the binary classification problem, notwithstanding its efficiency level. We believe this model can help investors in decision making as it provides a reliably tool to evaluate venture companies in the financial domain. For the future research, we perceive the need to enhance such areas as the variable selection process, the parameter selection of kernel function, the generalization, and the sample size of multi-class.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.10
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• pp.441-450
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• 2014

The stock market is constantly changing and sometimes the stock prices unaccountably plummet or surge. So, the stock market is recognized as a complex system and the change on the stock prices is unpredictable. Recently, many researchers try to understand the stock market as the network among individual stocks and to find a clue about the change of the stock prices from big data being created in real time from Internet. We focus on the correlation between the stock prices and the human interactions in Internet especially in the stock message boards. To uncover this correlation, we collected and investigated the articles concerning with 57 target companies, members of KOSPI200. From the analysis result, we found that there is no significant correlation between the stock prices and the article volume, but the strength of correlation between the article volume and the stock prices is relevant to the stock return. We propose a new method for recommending stock portfolio base on the result of our analysis. According to the simulated investment test using the article data from the stock message boards in 'Daum' portal site, the returns of our portfolio is about 1.55% per month, which is about 0.72% and 1.21% higher than that of the Markowitz's efficient portfolio and that of the KOSPI average respectively. Also, the case using the data from 'Naver' portal site, the stock returns of our proposed portfolio is about 0.90%, which is 0.35%, 0.40%, and 0.58% higher than those of our previous portfolio, Markowitz's efficient portfolio, and KOSPI average respectively. This study presents that collective human behavior on Internet stock message board can be much helpful to understand the stock market and the correlation between the stock price and the collective human behavior can be used to invest in stocks.

• Cartoon and Animation Studies
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• s.43
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• pp.47-75
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• 2016

This study has analyzed the history of the subcontract animation in Korea that began with Golden Bat of TBC Animation Division in 1966 to 1980s and shed the light on the history of subcontract animation that has been processed over 30 years in Korean animation. For this purpose, through the outlined status of subcontract animation, such as, production company, production status, scale of industry and so forth, the status of the OEM industry then has been checked and it links the solidified background of animation into subcontract production industry with the situation in time for analysis. In addition, on the basis of the foregoing, it is intended to broaden the horizon of the history of animation through the analysis on new search for facilitating the creative animation by overcoming the issues and limits generated by the subcontract animation industry. 1970s was the time that the national objective is to advance heavy-chemical industry and export-led economic growth. From the late 1970s, the animation has been spot lighted as the main-stream export industry through the overseas subcontract orders for animation. Expansion of the subcontract animation production has been influenced from the national policies on public culture, dispersion of color TV, facilitation of video production market and other media changes of the time that led the decline of animation audiences in theaters, and another cause would be in lack of platform of broadcasting companies that avoided the independent animation production for its economic theory. The subcontract animation industry may have the positive evaluation in the aspect of expanding the animation environment, such as, structuring of animation infra, development of new human resources and etc. However, the technology-incentive 'production'-oriented advancement has created distorted structure in advancing the professional human resources due to the absence of 'pre-production' of planning and others as well as the insufficient perception on 'post production (post work)', and it was unable to formulate domestic market by re-investing the capital accumulated for OEM industry into the production of creative animation and it has been assessed as negative aspect. Animation is a cultural and spiritual product of a country. Therefore, the systematic support policy for the facilitation of the creative animation, such as, development of professional human resources, creation of outstanding work, formation of market to make the pre-circulation structure and so forth has to be sought. However, animation is an industry, but there is no perception that it is a cultural industry based on the creativeness, not hardware-oriented manufacturing business. Such a lack of recognition, there was no policies to make the market and facilitate the creative animation by the animation of Korea for this period through the long-term plan and investment for independent work production. Such an attempt is newly begun through diverse searches for protection and advancement of creative animation in Korea after 1990s.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• Korean Journal of Construction Engineering and Management
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• v.17 no.5
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• pp.120-129
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• 2016

The user cost is an important analysis item which should be considered together with life-cycle of facility, administrator cost and discount rate in LCCA for efficient asset management of SOC facilities. Especially, a significant delay cost occurs often for users in the road field due to a work zone for cleaning and maintenance, and in such case, the administrator should consider the administrator cost as well as the user cost for more rational decision making. However, the user cost has not been considered in most decision making steps until recently and relevant studies also have not been carried out actively. In this study, the methodology to estimate the user cost and delay cost required in the decision making step using the traffic flow model and the direct benefit estimation model in the traffic facility investment evaluation guideline is suggested. And, the traffic flow model was estimated on 4 national highway sections where maintenance was actually carried out in 2014 using VISSIM and, the user cost and the delay cost were estimated based on the suggested methodology. The analysis result showed that the average user cost of $17,569,000KRW/km{\times}day$ occurred on Section A with approximately 30,000 AADT before a work zone occurred, and in case the first lane was blocked for maintenance, the delay cost of $10,193,000KRW/km{\times}day$ (158%) on average occurred additionally. The delay cost of $1,507,000KRW/km{\times}day$ (115%) and $1,985,000KRW/km{\times}day$ (119%) occurred on Sections B and D with approximately 20,000 AADT respectively and the delay cost of $262,000KRW/km{\times}day$ (105%) occurred on Section C with approximately 10,000 AADT. This result of this study was estimated based on the simulation of traffic flow model so that there is a limitation in its actual application. A study ot develop a highly appropriate model using actual observation data and improve the possibility to apply it through the verification using the simulation will be necessary in future.

• Korean Journal of Construction Engineering and Management
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• v.10 no.5
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• pp.37-46
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• 2009

This research will focus on the public agencies, designers, supervisors, building cooperation, and contractor who involved in urban development plan. By understanding the complexity and the priorities in urban development process, all problems of the urban development projects can be solved or improved. These priorities are specified using AHP (Analytic Hierarchy Process). A questionnaire survey is employed to identify the problems of urban development process and the methods of revitalizing urban in this research. Through the survey, 35 issues are drawn out. Factor analysis technique is applied to extract the underlying interrelationships possibly existing. Using latent root criterion and varimax rotation method, 9 factors are extracted(by using 34 issues after deleting 1 issue less than 0.4 of factor loading) These 9 factors named as PIF (Problem Improvement Factor) consist of integration estimation (PIF1), cooperation operation capability (PIF2), contractor corporation capability (PIF3), capital for infrastructure investment (PIF4), misunderstanding of effective land use (PIF5), financial capability (PIF6), obscure goal of project (PIF7), shortage of cooperation expertise (PIFS), administrative procedures (PIF9). PIF 6 is the most important factor and PIF 1 is the most widely effective factor to succeed urban land development projects. It is recognized that administrative office is most responsible for PIF1 cooperation is most responsible for PIF2, 7, 8 and 9; contractors is most responsible for PIF3 and PIF6; administrative agencies is most responsible for PIF4; cooperation and consultants are responsible for PIF5. From findings in this study, some suggestions are proposed for the revitalization methods of urban development projects through the land readjustment method.

• Journal of Korean Society of Forest Science
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• v.99 no.4
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• pp.611-617
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• 2010

The current studies aim is to analyze the production cost of walnut tree cultivation and its object was targeted at walnut tree cultivating household region 163. The analysis is as follows. Our domestic walnut tree cultivating households averagely have cultivated about 0.7ha, and planting number per ha was averagely 204, and it showed that compared to the standard planting number (100), the plantation was done close planted. The most cultivar cultivated according to regions were Chungbuk region: sangchon 65.7%, Chungnam region: kwangduk 68.6%, Jeonbuk region: sangchon 98.0%, Gyeongbuk region: daeboo 61.2%. The production cost for cultivating walnuts can be classified into the followings; management cost(4436 thousand won/ha) such as manufacturing cost(292 thousand won/ha), intermediate material cost(3682 thousand won/ha), rent(103 thousand won/ha), employment cost(653 thousand won/ha) etc, and self-serviced expenses such as self-laboring cost(5,834 thousand won/ha), land security cost(490 thousand won/ha), fixed capital cost(834 thousand won/ha), circulating capital cost(234 thousand won/ha) etc. 11,820 thousand won were invested for the production cost of walnut and it made 11,586 thousand won/ha(rate of investment 72.3%) profit, and the net income was 4,196 thousand won/ha(net income rate 26.2%), showing high amount of income. The manufactured walnuts were marketed in Nong-hyup 39.8%, wholesalers 20.8%, dealers 19.8% and recently, as the amount of goods marketed directly to consumers themselves have increased, the income has reached up to 18.9%. At the basis of making most of idle soil, walnut tree's cultivated regions are fairly small, and due to the characteristics of sideline management, it has its limits in searching for production policy locally and promotion strategy of industries. Therefore, if the basic database can be established, subjected only to full-time cultivating households, then not only would the differences between the imported walnuts be reinforced, it would also be possible to transfer into the new and improved distribution system. Furthermore, through establishment of the database, it can be anticipated that it would contribute greatly in the increase of the household income.

• Journal of Energy Engineering
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• v.28 no.3
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• pp.65-79
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• 2019

The study attempted to estimate the optimal facility capacity by combining renewable energy sources that can be connected with gas CHP in industrial complexes. In particular, we reviewed industrial complexes subject to energy use plan from 2013 to 2016. Although the regional designation was excluded, Sejong industrial complex, which has a fuel usage of 38 thousand TOE annually and a high heat density of $92.6Gcal/km^2{\cdot}h$, was selected for research. And we analyzed the optimal operation model of CHP Hybrid System linking fuel cell and photovoltaic power generation using HOMER Pro, a renewable energy hybrid system economic analysis program. In addition, in order to improve the reliability of the research by analyzing not only the heat demand but also the heat demand patterns for the dominant sectors in the thermal energy, the main supply energy source of CHP, the economic benefits were added to compare the relative benefits. As a result, the total indirect heat demand of Sejong industrial complex under construction was 378,282 Gcal per year, of which paper industry accounted for 77.7%, which is 293,754 Gcal per year. For the entire industrial complex indirect heat demand, a single CHP has an optimal capacity of 30,000 kW. In this case, CHP shares 275,707 Gcal and 72.8% of heat production, while peak load boiler PLB shares 103,240 Gcal and 27.2%. In the CHP, fuel cell, and photovoltaic combinations, the optimum capacity is 30,000 kW, 5,000 kW, and 1,980 kW, respectively. At this time, CHP shared 275,940 Gcal, 72.8%, fuel cell 12,390 Gcal, 3.3%, and PLB 90,620 Gcal, 23.9%. The CHP capacity was not reduced because an uneconomical alternative was found that required excessive operation of the PLB for insufficient heat production resulting from the CHP capacity reduction. On the other hand, in terms of indirect heat demand for the paper industry, which is the dominant industry, the optimal capacity of CHP, fuel cell, and photovoltaic combination is 25,000 kW, 5,000 kW, and 2,000 kW. The heat production was analyzed to be CHP 225,053 Gcal, 76.5%, fuel cell 11,215 Gcal, 3.8%, PLB 58,012 Gcal, 19.7%. However, the economic analysis results of the current electricity market and gas market confirm that the return on investment is impossible. However, we confirmed that the CHP Hybrid System, which combines CHP, fuel cell, and solar power, can improve management conditions of about KRW 9.3 billion annually for a single CHP system.

• The Korean Journal of Air & Space Law and Policy
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• v.33 no.2
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• pp.3-34
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• 2018

Korea's air transport industry has a 70-year history since Korea National Airline was establishment in October 1948. Korea has 9 airlines which have international air transport business licenses, and as of 2017, air transport performance(Domestic & International) is ranked 8th in the world. Through analysis of Korea's air transport industry, this paper examines the essential problems of the domestic air transport industry and what policies and laws should be supplemented, and presents an "Policy Directions for the Air Transport Industry" that can continue to grow into a global aviation leading country in the future. Analysis of aviation statistics shows that the nation's air transport industry has a very high growth rate, and national airlines continue to invest in sustainable growth. Furthermore, new companies are also trying to enter the market. As of November 2018, four companies applied for licenses for international air transport business, one for international air transport business (cargo) license, and the Ministry of Land, Infrastructure and Transport is expected to decide whether to issue the license by first quarter of 2019. While some expect price reductions and consumer benefits through competition promotion, others worry about worsening airline financial structures and reducing safety investment due to competition. To sum up the problems of the nation's air transport industry, first, low-cost airlines focus only on attracting domestic demand, and thus have a weak foundation for continued growth. Second, the rapid growth in recent years has led to the lack of aviation professionals such as pilots and technicians and the saturation of slots at major airports. Third, since the financial soundness of airlines is not systematically managed, the financial situation of airlines can quickly deteriorate and the damage can be attributed to consumers. In order for the national airlines to continue to develop, the first is to focus on the endless demand of the global aviation market and to secure international competitiveness. Second, the government should support the airline infrastructure according to the size of the air transport industry, third, we will systematically nurture aviation experts who will lead the future of the nation's air transport industry, and finally, the government will have to continuously manage the financial status of airlines to prevent consumer damage in advance. Nowadays the air transport industry has become very competitive. Not only do airlines have to work hard for the sustainable development of national airlines, but all government agencies must support our airline companies in policy to win international competition.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.33-49
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• 2018

The impact of TV Drama success on TV Rating and the channel promotion effectiveness is very high. The cultural and business impact has been also demonstrated through the Korean Wave. Therefore, the early prediction of the blockbuster success of TV Drama is very important from the strategic perspective of the media industry. Previous studies have tried to predict the audience ratings and success of drama based on various methods. However, most of the studies have made simple predictions using intuitive methods such as the main actor and time zone. These studies have limitations in predicting. In this study, we propose a model for predicting the popularity of drama by analyzing the customer's viewing pattern based on various theories. This is not only a theoretical contribution but also has a contribution from the practical point of view that can be used in actual broadcasting companies. In this study, we collected data of 280 TV mini-series dramas, broadcasted over the terrestrial channels for 10 years from 2003 to 2012. From the data, we selected the most highly ranked and the least highly ranked 45 TV drama and analyzed the viewing patterns of them by 11-step. The various assumptions and conditions for modeling are based on existing studies, or by the opinions of actual broadcasters and by data mining techniques. Then, we developed a prediction model by measuring the viewing-time distance (difference) using Euclidean and Correlation method, which is termed in our study similarity (the sum of distance). Through the similarity measure, we predicted the success of dramas from the viewer's initial viewing-time pattern distribution using 1~5 episodes. In order to confirm that the model is shaken according to the measurement method, various distance measurement methods were applied and the model was checked for its dryness. And when the model was established, we could make a more predictive model using a grid search. Furthermore, we classified the viewers who had watched TV drama more than 70% of the total airtime as the "passionate viewer" when a new drama is broadcasted. Then we compared the drama's passionate viewer percentage the most highly ranked and the least highly ranked dramas. So that we can determine the possibility of blockbuster TV mini-series. We find that the initial viewing-time pattern is the key factor for the prediction of blockbuster dramas. From our model, block-buster dramas were correctly classified with the 75.47% accuracy with the initial viewing-time pattern analysis. This paper shows high prediction rate while suggesting audience rating method different from existing ones. Currently, broadcasters rely heavily on some famous actors called so-called star systems, so they are in more severe competition than ever due to rising production costs of broadcasting programs, long-term recession, aggressive investment in comprehensive programming channels and large corporations. Everyone is in a financially difficult situation. The basic revenue model of these broadcasters is advertising, and the execution of advertising is based on audience rating as a basic index. In the drama, there is uncertainty in the drama market that it is difficult to forecast the demand due to the nature of the commodity, while the drama market has a high financial contribution in the success of various contents of the broadcasting company. Therefore, to minimize the risk of failure. Thus, by analyzing the distribution of the first-time viewing time, it can be a practical help to establish a response strategy (organization/ marketing/story change, etc.) of the related company. Also, in this paper, we found that the behavior of the audience is crucial to the success of the program. In this paper, we define TV viewing as a measure of how enthusiastically watching TV is watched. We can predict the success of the program successfully by calculating the loyalty of the customer with the hot blood. This way of calculating loyalty can also be used to calculate loyalty to various platforms. It can also be used for marketing programs such as highlights, script previews, making movies, characters, games, and other marketing projects.