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.
Because of differences in thoughts and ideology, our country, Korea has been deprived of national unity for some thirty years of time and tide. To achieve peaceful unification, the cultivation of national strength is of paramount importance. This national strength is also essential if Korea is to take rightful place in the international societies and to have the confidence of these societies. However, national strength can never be achieved in a short time. The fundamental elements in economic development that are directly conducive to the cultivation of national strength can be said to lie in -a stable political system, -exertion of powerful leadership, -cultivation of a spirit of diligence, self-help and cooperation, -modernization of human brain power, and -establishment of a scientific and well planned economic policy and strong enforcement of this policy. Our country, Korea, has attained brilliant economic development in the past 15 years under the strong leadership of president Park Chung Hee. However, there are still many problems to be solved. A few of them are: -housing and home problems, -increasing demand for employment, -increasing demand for staple food and -the need to improve international balance of payment. Solution of the above mentioned problems requires step by step scientific development of each sector and region of our contry. As a spearhead project in regional development, the Saemaul Campaign or new village movement can be cited. The campaign is now spreading throughout the country like a grass fire. However, such campaigns need considerable encouragement and support and the means for the desired development must be provided if the regional and sectoral development program is to sucdceed. The construction of large multipurpose dams in major river basin plays significant role in all aspects of national, regional and sectoral development. It ensures that the water resource, for which there is no substitute, is retained and utilized for irrigation of agricultural areas, production of power for industry, provision of water for domestic and industrial uses and control of river water. Water is the very essence of life and we must conserve and utilize what we have for the betterment of our peoples and their heir. The regional and social impact of construction of a large dam is enormous. It is intended to, and does, dras tically improve the "without-project" socio-economic conditions. A good example of this is the Soyanggang multipurpose dam. This project will significantly contribute to our national strength by utilizing the stored water for the benefit of human life and relief of flood and drought damages. Annual average precipitation in Korea is 1160mm, a comparatively abundant amount. The catchment areas of the Han River, Keum River, and Youngsan River are $62,755\textrm{km}^2$, accounting for 64% of the national total. Approximately 62% of the national population inhabits in this area, and 67% of the national gross product comes from the area. The annual population growth rate of the country is currently estimated at 1.7%, and every year the population growth in urban area increases at a rising rate. The population of Seoul, Pusan, and Taegu, the three major cities in Korea, is equal to one third of our national total. According to the census conducted on October 1, 1975, the population in the urban areas has increased by 384,000, whereas that in rural areas has decreased by 59,000,000 in the past five years. The composition of population between urban and rural areas varied from 41%~59% in 1959 to 48%~52% in 1975. To mitigate this treand towards concentration of population in urban areas, employment opportunities must be provided in regional and rural areas. However, heavy and chemical industries, which mitigate production and employment problems at the same time, must have abundant water and energy. Also increase in staple food production cannot be attained without water. At this point in time, when water demand is rapidly growing, it is essential for the country to provide as much a reservoir capacity as possible to capture the monsoon rainfall, which concentarated in the rainy seaon from June to Septesmber, and conserve the water for year round use. The floods, which at one time we called "the devil" have now become a source of immense benefit to Korea. Let me explain the topographic condition in Korea. In northern and eastern areas we have high mountains and rugged country. Our rivers originate in these mountains and flow in a general southerly or westerly direction throught ancient plains. These plains were formed by progressive deposition of sediments from the mountains and provide our country with large areas of fertile land, emminently suited to settlement and irrigated agricultural development. It is, therefore, quite natural that these areas should become the polar point for our regional development program. Hower, we are fortunate in that we have an additional area or areas, which can be used for agricultural production and settlement of our peoples, particularly those peoples who may be displaced by the formation of our reservoirs. I am speaking of the tidelands along the western and southern coasts. The other day the Ministry of Agriculture and Fishery informed the public of a tideland reclamation of which 400,000 hectares will be used for growing rice as part of our national food self-sufficiency programme. Now, again, we arrive at the need for water, as without it we cannot realize this ambitious programme. And again we need those dams to provide it. As I mentioned before, dams not only provide us with essential water for agriculture, domestic and industrial use, but provide us with electrical energy, as it is generally extremely economical to use the water being release for the former purposes to drive turbines and generators. At the present time we have 13 hydro-electric power plants with an installed capacity of 711,000 kilowatts equal to 16% of our national total. There are about 110 potential dams ites in the country, which could yield about 2,300,000 kilowatts of hydro-electric power. There are about 54 sites suitable for pumped storage which could produce a further 38,600,000 kilowatts of power. All available if we carefully develop our water resources. To summarize, water resource development is essential to the regional development program and the welfare of our people, it must proceed hand-in-hand with other aspects of regional development such as land impovement, high way extension, development of our forests, erosion control, and develop ment of heavy and chemical industries. Through the successful implementation of such an integrated regional development program, we can look forward to a period of national strength, and due recognition of our country by the worlds societies.
This paper estimates the impact of Japan's economic sanction on DPRK trade in the 2000s. It conceptualizes the effects of sanction on DPRK trade, econometrically tests whether such effects exist in case of Japan's sanction using currently available DPRK trade statistics, and measures the size of the effects by correcting and reconfiguring the deficiencies of the currently available DPRK trade statistics. The main findings of the paper are as follows. First, Japan's sanction can have two different effects on DPRK trade: 'Sanction Country Effect' and "Third Country Effect.' The former means that the sanction diminishes DPRK trade with Japan while the latter refers to the effects on DPRK trade with other countries as well. The third country effect can arise not simply because the DPRK changes its trade routes to circumvent the sanction, but because the sanction forces the DPRK to readjust its major trade items and patterns. Second, currently no official DPRK trade statistics are available. Thus, the so-called mirror data referring to DPRK trading partners' statistics should be employed for the analysis of the sanction effects. However, all currently available mirror data suffer from three fundamental problems: 1) they may omit the real trade partners of the DPRK; 2) they may confuse ROK trade with DPRK trade; 3) they cannot distinguish non-commercial trade from commercial trade, whereas only the latter concerns Japan's sanction. Considering those problems, we have to adopt the following method in order to reach a reasonable conclusion about the sanction effect. That is, we should repeat the same analysis using all different mirror data currently available, which include KOTRA, IMF and UN Commodity Trade Statistics, and then discuss only the common results from them. Third, currently available mirror data make the following points. 1) DPRK trade is well explained by the gravity model. 2) Japan's sanction has not only the sanction country effect but also the third country effect on DPRK trade. 3) The third country effect occurs differently on DPRK export and import. In case of export, the mirror statistics reveal positive (+) third country effects on all of the major trade partners of the DPRK, including South Korea, China and Thailand. However, on DPRK import, such third country effects are not statistically significant even for South Korea and China. 4) This suggests that Japan's sanction has greater effects on DPRK import rather than its export. Fourth, as far as DPRK export is concerned, it is possible to resolve the abovementioned fundamental problems of mirror data and thus reconstruct more accurate statistics on DPRK trade. Those reconstructed statistics lead us to following conclusions. 1) Japan's economic sanction diminished DPRK's export to Japan from 2004 to 2006 by 103 million dollars on annual average (Sanction Country Effect). It comprises around 60 percent of DPRK's export to Japan in 2003. 2) However, for the same period, the DPRK diverted its exports to other countries to cope up with Japan's sanction, and as a result its export to other countries increased by 85 million dollars on annual average (Third Country Effect). 3) This means that more than 80 per cent of the sanction country effect was made up for by the third country effect. And the actual size of impact that Japan's sanction made on DPRK export in total was merely 30 million dollars on annual average. 4) The third country effect occurred mostly in inter-Korean trade. In fact, Japan's sanction increased DPRK export to the ROK by 72 million dollars on annual average. In contrast, there was no statistically significant increase in DPRK export to China caused by Japan's sanction. 5) It means that the DPRK confronted Japan's sanction and mitigated its impact primarily by using inter-Korean trade and thus the ROK. Fifth, two things should be noted concerning the fourth results above. 1) The results capture the third country effect caused only by trade transfer. Facing Japan's sanction, the DPRK could transfer its existing trade with Japan to other countries. Also it could change its main export items and increase the export of those new items to other countries as mentioned in the first result. However, the fourth results above reflect only the former, not the latter. 2) Although Japan's sanction did not make a huge impact on DPRK export, it might not be necessarily true for DPRK import. Indeed the currently available mirror statistics suggest that Japan's sanction has greater effects on DPRK import. Hence it would not be wise to argue that Japan's sanction did not have much impact on DPRK trade in general, simply using the fourth result above.
Social media is a representative form of the Web 2.0 that shapes the change of a user's information behavior by allowing users to produce their own contents without any expert skills. In particular, as a new communication medium, it has a profound impact on the social change by enabling users to communicate with the masses and acquaintances their opinions and thoughts. Social media data plays a significant role in an emerging Big Data arena. A variety of research areas such as social network analysis, opinion mining, and so on, therefore, have paid attention to discover meaningful information from vast amounts of data buried in social media. Social media has recently become main foci to the field of Information Retrieval and Text Mining because not only it produces massive unstructured textual data in real-time but also it serves as an influential channel for opinion leading. But most of the previous studies have adopted broad-brush and limited approaches. These approaches have made it difficult to find and analyze new information. To overcome these limitations, we developed a real-time Twitter trend mining system to capture the trend in real-time processing big stream datasets of Twitter. The system offers the functions of term co-occurrence retrieval, visualization of Twitter users by query, similarity calculation between two users, topic modeling to keep track of changes of topical trend, and mention-based user network analysis. In addition, we conducted a case study on the 2012 Korean presidential election. We collected 1,737,969 tweets which contain candidates' name and election on Twitter in Korea (http://www.twitter.com/) for one month in 2012 (October 1 to October 31). The case study shows that the system provides useful information and detects the trend of society effectively. The system also retrieves the list of terms co-occurred by given query terms. We compare the results of term co-occurrence retrieval by giving influential candidates' name, 'Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn' as query terms. General terms which are related to presidential election such as 'Presidential Election', 'Proclamation in Support', Public opinion poll' appear frequently. Also the results show specific terms that differentiate each candidate's feature such as 'Park Jung Hee' and 'Yuk Young Su' from the query 'Guen Hae Park', 'a single candidacy agreement' and 'Time of voting extension' from the query 'Jae In Moon' and 'a single candidacy agreement' and 'down contract' from the query 'Chul Su Ahn'. Our system not only extracts 10 topics along with related terms but also shows topics' dynamic changes over time by employing the multinomial Latent Dirichlet Allocation technique. Each topic can show one of two types of patterns-Rising tendency and Falling tendencydepending on the change of the probability distribution. To determine the relationship between topic trends in Twitter and social issues in the real world, we compare topic trends with related news articles. We are able to identify that Twitter can track the issue faster than the other media, newspapers. The user network in Twitter is different from those of other social media because of distinctive characteristics of making relationships in Twitter. Twitter users can make their relationships by exchanging mentions. We visualize and analyze mention based networks of 136,754 users. We put three candidates' name as query terms-Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn'. The results show that Twitter users mention all candidates' name regardless of their political tendencies. This case study discloses that Twitter could be an effective tool to detect and predict dynamic changes of social issues, and mention-based user networks could show different aspects of user behavior as a unique network that is uniquely found in Twitter.
People easily believe that news and stock index are closely related. They think that securing news before anyone else can help them forecast the stock prices and enjoy great profit, or perhaps capture the investment opportunity. However, it is no easy feat to determine to what extent the two are related, come up with the investment decision based on news, or find out such investment information is valid. If the significance of news and its impact on the stock market are analyzed, it will be possible to extract the information that can assist the investment decisions. The reality however is that the world is inundated with a massive wave of news in real time. And news is not patterned text. This study suggests the stock-index invest model based on "News Big Data" opinion mining that systematically collects, categorizes and analyzes the news and creates investment information. To verify the validity of the model, the relationship between the result of news opinion mining and stock-index was empirically analyzed by using statistics. Steps in the mining that converts news into information for investment decision making, are as follows. First, it is indexing information of news after getting a supply of news from news provider that collects news on real-time basis. Not only contents of news but also various information such as media, time, and news type and so on are collected and classified, and then are reworked as variable from which investment decision making can be inferred. Next step is to derive word that can judge polarity by separating text of news contents into morpheme, and to tag positive/negative polarity of each word by comparing this with sentimental dictionary. Third, positive/negative polarity of news is judged by using indexed classification information and scoring rule, and then final investment decision making information is derived according to daily scoring criteria. For this study, KOSPI index and its fluctuation range has been collected for 63 days that stock market was open during 3 months from July 2011 to September in Korea Exchange, and news data was collected by parsing 766 articles of economic news media M company on web page among article carried on stock information>news>main news of portal site Naver.com. In change of the price index of stocks during 3 months, it rose on 33 days and fell on 30 days, and news contents included 197 news articles before opening of stock market, 385 news articles during the session, 184 news articles after closing of market. Results of mining of collected news contents and of comparison with stock price showed that positive/negative opinion of news contents had significant relation with stock price, and change of the price index of stocks could be better explained in case of applying news opinion by deriving in positive/negative ratio instead of judging between simplified positive and negative opinion. And in order to check whether news had an effect on fluctuation of stock price, or at least went ahead of fluctuation of stock price, in the results that change of stock price was compared only with news happening before opening of stock market, it was verified to be statistically significant as well. In addition, because news contained various type and information such as social, economic, and overseas news, and corporate earnings, the present condition of type of industry, market outlook, the present condition of market and so on, it was expected that influence on stock market or significance of the relation would be different according to the type of news, and therefore each type of news was compared with fluctuation of stock price, and the results showed that market condition, outlook, and overseas news was the most useful to explain fluctuation of news. On the contrary, news about individual company was not statistically significant, but opinion mining value showed tendency opposite to stock price, and the reason can be thought to be the appearance of promotional and planned news for preventing stock price from falling. Finally, multiple regression analysis and logistic regression analysis was carried out in order to derive function of investment decision making on the basis of relation between positive/negative opinion of news and stock price, and the results showed that regression equation using variable of market conditions, outlook, and overseas news before opening of stock market was statistically significant, and classification accuracy of logistic regression accuracy results was shown to be 70.0% in rise of stock price, 78.8% in fall of stock price, and 74.6% on average. This study first analyzed relation between news and stock price through analyzing and quantifying sensitivity of atypical news contents by using opinion mining among big data analysis techniques, and furthermore, proposed and verified smart investment decision making model that could systematically carry out opinion mining and derive and support investment information. This shows that news can be used as variable to predict the price index of stocks for investment, and it is expected the model can be used as real investment support system if it is implemented as system and verified in the future.
Recently due to the advancements of science and information technology, the socio-economic business areas are changing from the industrial economy to a knowledge economy. Furthermore, companies need to do creation of new value through continuous innovation, development of core competencies and technologies, and technological convergence. Therefore, the identification of major trends in technology research and the interdisciplinary knowledge-based prediction of integrated technologies and promising techniques are required for firms to gain and sustain competitive advantage and future growth engines. The aim of this paper is to understand the recent research trend in management of technology (MOT) and to foresee promising technologies with deep knowledge for both technology and business. Furthermore, this study intends to give a clear way to find new technical value for constant innovation and to capture core technology and technology convergence. Bibliometrics is a metrical analysis to understand literature's characteristics. Traditional bibliometrics has its limitation not to understand relationship between trend in technology management and technology itself, since it focuses on quantitative indices such as quotation frequency. To overcome this issue, the network focused bibliometrics has been used instead of traditional one. The network focused bibliometrics mainly uses "Co-citation" and "Co-word" analysis. In this study, a keywords network analysis, one of social network analysis, is performed to analyze recent research trend in MOT. For the analysis, we collected keywords from research papers published in international journals related MOT between 2002 and 2011, constructed a keyword network, and then conducted the keywords network analysis. Over the past 40 years, the studies in social network have attempted to understand the social interactions through the network structure represented by connection patterns. In other words, social network analysis has been used to explain the structures and behaviors of various social formations such as teams, organizations, and industries. In general, the social network analysis uses data as a form of matrix. In our context, the matrix depicts the relations between rows as papers and columns as keywords, where the relations are represented as binary. Even though there are no direct relations between papers who have been published, the relations between papers can be derived artificially as in the paper-keyword matrix, in which each cell has 1 for including or 0 for not including. For example, a keywords network can be configured in a way to connect the papers which have included one or more same keywords. After constructing a keywords network, we analyzed frequency of keywords, structural characteristics of keywords network, preferential attachment and growth of new keywords, component, and centrality. The results of this study are as follows. First, a paper has 4.574 keywords on the average. 90% of keywords were used three or less times for past 10 years and about 75% of keywords appeared only one time. Second, the keyword network in MOT is a small world network and a scale free network in which a small number of keywords have a tendency to become a monopoly. Third, the gap between the rich (with more edges) and the poor (with fewer edges) in the network is getting bigger as time goes on. Fourth, most of newly entering keywords become poor nodes within about 2~3 years. Finally, keywords with high degree centrality, betweenness centrality, and closeness centrality are "Innovation," "R&D," "Patent," "Forecast," "Technology transfer," "Technology," and "SME". The results of analysis will help researchers identify major trends in MOT research and then seek a new research topic. We hope that the result of the analysis will help researchers of MOT identify major trends in technology research, and utilize as useful reference information when they seek consilience with other fields of study and select a new research topic.
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.
Internet commerce has been growing at a rapid pace for the last decade. Many firms try to reach wider consumer markets by adding the Internet channel to the existing traditional channels. Despite the various benefits of the Internet channel, a significant number of firms failed in managing the new type of channel. Previous studies could not cleary explain these conflicting results associated with the Internet channel. One of the major reasons is most of the previous studies conducted analyses under a specific market condition and claimed that as the impact of Internet channel introduction. Therefore, their results are strongly influenced by the specific market settings. However, firms face various market conditions in the real worlddensity and disutility of using the Internet. The purpose of this study is to investigate the impact of various market environments on a firm's optimal channel strategy by employing a flexible game theory model. We capture various market conditions with consumer density and disutility of using the Internet.
shows the channel structures analyzed in this study. Before the Internet channel is introduced, a monopoly manufacturer sells its products through an independent physical store. From this structure, the manufacturer could introduce its own Internet channel (MI). The independent physical store could also introduce its own Internet channel and coordinate it with the existing physical store (RI). An independent Internet retailer such as Amazon could enter this market (II). In this case, two types of independent retailers compete with each other. In this model, consumers are uniformly distributed on the two dimensional space. Consumer heterogeneity is captured by a consumer's geographical location (ci) and his disutility of using the Internet channel (${\delta}_{N_i}$).
shows various market conditions captured by the two consumer heterogeneities.
(a) illustrates a market with symmetric consumer distributions. The model captures explicitly the asymmetric distributions of consumer disutility in a market as well. In a market like that is represented in
(c), the average consumer disutility of using an Internet store is relatively smaller than that of using a physical store. For example, this case represents the market in which 1) the product is suitable for Internet transactions (e.g., books) or 2) the level of E-Commerce readiness is high such as in Denmark or Finland. On the other hand, the average consumer disutility when using an Internet store is relatively greater than that of using a physical store in a market like (b). Countries like Ukraine and Bulgaria, or the market for "experience goods" such as shoes, could be examples of this market condition.
summarizes the various scenarios of consumer distributions analyzed in this study. The range for disutility of using the Internet (${\delta}_{N_i}$) is held constant, while the range of consumer distribution (${\chi}_i$) varies from -25 to 25, from -50 to 50, from -100 to 100, from -150 to 150, and from -200 to 200.
summarizes the analysis results. As the average travel cost in a market decreases while the average disutility of Internet use remains the same, average retail price, total quantity sold, physical store profit, monopoly manufacturer profit, and thus, total channel profit increase. On the other hand, the quantity sold through the Internet and the profit of the Internet store decrease with a decreasing average travel cost relative to the average disutility of Internet use. We find that a channel that has an advantage over the other kind of channel serves a larger portion of the market. In a market with a high average travel cost, in which the Internet store has a relative advantage over the physical store, for example, the Internet store becomes a mass-retailer serving a larger portion of the market. This result implies that the Internet becomes a more significant distribution channel in those markets characterized by greater geographical dispersion of buyers, or as consumers become more proficient in Internet usage. The results indicate that the degree of price discrimination also varies depending on the distribution of consumer disutility in a market. The manufacturer in a market in which the average travel cost is higher than the average disutility of using the Internet has a stronger incentive for price discrimination than the manufacturer in a market where the average travel cost is relatively lower. We also find that the manufacturer has a stronger incentive to maintain a high price level when the average travel cost in a market is relatively low. Additionally, the retail competition effect due to Internet channel introduction strengthens as average travel cost in a market decreases. This result indicates that a manufacturer's channel power relative to that of the independent physical retailer becomes stronger with a decreasing average travel cost. This implication is counter-intuitive, because it is widely believed that the negative impact of Internet channel introduction on a competing physical retailer is more significant in a market like Russia, where consumers are more geographically dispersed, than in a market like Hong Kong, that has a condensed geographic distribution of consumers. illustrates how this happens. When mangers consider the overall impact of the Internet channel, however, they should consider not only channel power, but also sales volume. When both are considered, the introduction of the Internet channel is revealed as more harmful to a physical retailer in Russia than one in Hong Kong, because the sales volume decrease for a physical store due to Internet channel competition is much greater in Russia than in Hong Kong. The results show that manufacturer is always better off with any type of Internet store introduction. The independent physical store benefits from opening its own Internet store when the average travel cost is higher relative to the disutility of using the Internet. Under an opposite market condition, however, the independent physical retailer could be worse off when it opens its own Internet outlet and coordinates both outlets (RI). This is because the low average travel cost significantly reduces the channel power of the independent physical retailer, further aggravating the already weak channel power caused by myopic inter-channel price coordination. The results implies that channel members and policy makers should explicitly consider the factors determining the relative distributions of both kinds of consumer disutility, when they make a channel decision involving an Internet channel. These factors include the suitability of a product for Internet shopping, the level of E-Commerce readiness of a market, and the degree of geographic dispersion of consumers in a market. Despite the academic contributions and managerial implications, this study is limited in the following ways. First, a series of numerical analyses were conducted to derive equilibrium solutions due to the complex forms of demand functions. In the process, we set up V=100, ${\lambda}$=1, and ${\beta}$=0.01. Future research may change this parameter value set to check the generalizability of this study. Second, the five different scenarios for market conditions were analyzed. Future research could try different sets of parameter ranges. Finally, the model setting allows only one monopoly manufacturer in the market. Accommodating competing multiple manufacturers (brands) would generate more realistic results.
The film Moranbong is the product of a trip to North Korea in 1958, when Armangati, Chris Marker, Claude Lantzmann, Francis Lemarck and Jean-Claude Bonardo left at the invitation of Joseon Film. However, for political reasons, the film was not immediately released, and it was not until 2010 that it was rediscovered and received attention. The movie consists of the narratives of Young-ran and Dong-il, set in the Korean War, that are folded into the narratives of Chunhyang and Mongryong in the classic Chunhyangjeon of Joseon. At this time, Joseon's classics are reproduced in the form of the drama Chunhyangjeon, which shares the time zone with the two main characters, and the two narratives are covered in a total of six scenes. There are two layers of middle-story frames in the movie, and if the same narrative is set in North Korea in the 1950s, there is an epic produced by the producers and actors of the Changgeuk Chunhyangjeon and the Changgeuk Chunhyangjeon as a complete work. In the outermost frame of the movie, Dong-il is the main character, but in the inner double frame, Young-ran, who is an actor growing up with the Changgeuk Chunhyangjeon and a character in the Changgeuk Chunhyangjeon, is the center. The following three OST albums are Corée Moranbong released in France in 1960, Musique de corée released in 1970, and 朝鮮の伝統音樂-唱劇 「春香伝」と伝統樂器- released in 1968 in Japan. While Corée Moranbong consists only of the music from the film Moranbong, the two subsequent albums included additional songs collected and recorded by Pyongyang National Broadcasting System. However, there is no information about the movie Moranbong on the album released in Japan. Under the circumstances, it is highly likely that the author of the record label or music commentary has not confirmed the existence of the movie Moranbong, and may have intentionally excluded related contents due to the background of the film's ban on its release. The results of analyzing the detailed scenes of the Changgeuk Chunhyangjeon, Farewell Song, Sipjang-ga, Chundangsigwa, Bakseokti and Prison Song in the movie Moranbong or OST album in the 1950s are as follows. First, the process of establishing the North Korean Changgeuk Chunhyangjeon in the 1950s was confirmed. The play, compiled in 1955 through the Joseon Changgeuk Collection, was settled in the form of a Changgeuk that can be performed in the late 1950s by the Changgeuk Chunhyangjeon between 1956 and 1958. Since the 1960s, Chunhyangjeon has no longer been performed as a traditional pansori-style Changgeuk, so the film Moranbong and the album Corée moranbong are almost the last records to capture the Changgeuk Chunhyangjeon and its music. Second, we confirmed the responses of the actors to the controversy over Takseong in the North Korean creative world in the 1950s. Until 1959, there was a voice of criticism surrounding Takseong and a voice of advocacy that it was also a national characteristic. Shin Woo-sun, who almost eliminated Takseong with clear and high-pitched phrases, air man who changed according to the situation, who chose Takseong but did not actively remove Takseong, Lim So-hyang, who tried to maintain his own tone while accepting some of modern vocalization. Although Cho Sang-sun and Lim So-hyang were also guaranteed roles to continue their voices, the selection/exclusion patterns in the movie Moranbong were linked to the Takseong removal guidelines required by North Korean musicians in the name of Dang and People in the 1950s. Second, Changgeuk actors' response to the controversy over the turbidity of the North Korean Changgeuk community in the 1950s was confirmed. Until 1959, there were voices of criticism and support surrounding Taksung in North Korea. Shin Woo-sun, who showed consistent performance in removing turbidity with clear, high-pitched vocal sounds, Gong Gi-nam, who did not actively remove turbidity depending on the situation, Cho Sang-sun, who accepted some of the vocalization required by the party, while maintaining his original tone. On the other hand, Cho Sang-seon and Lim So-hyang were guaranteed roles to continue their sounds, but the selection/exclusion patterns of Moranbong was independently linked to the guidelines for removing turbidity that the Gugak musicians who crossed to North Korea had been asked for.
The genetic diagnosis methods by RT-PCR and Virion capture (VC)/RT-PCR against Rice stripe virus (RSV) were developed. Three diagnosis methods of seedling test, ELISA and RT-PCR were compared in virus detection sensitivity (VDS) for RSV. The VDS of ELISA for RSV viruliferous small brown plant hopper (SBPH) was higher with 40.5% than that of seedling test. The VDS of RT-PCR was higher with 21% than that of ELISA. The VDS of ELISA and VC/RT-PCR was same with 9.2% in average on the SBPH collected from fields at the areas of Gimpo, Pyungtaeg and Sihueng, Gyeonggi province in 2009. The specific primers of RSV for SBPH and rice plant were developed for the diagnosis by Real time PCR. The RQ value of Real time PCR for the viruliferous and non viruliferous SBPH was 1 for 50 heads of non viruliferous SBPH, 96.5 for 50 heads of viruliferous SBPH, 23.1 for 10 heads of viruliferous SBPH + 40 heads of non viruliferous SBPH, and 75.6 for 30 heads of viruliferous SBPH + 20 heads of non viruliferous SBPH. The RQ value was increased positively by the ratio of viruliferous SBPH. Full sequences of 4 genomes of RSV RNA1, RNA2, RNA3 and RNA4 were analysed for the 13 RSV isolates from rice plants collected from different areas. Genetic relationships among the RSV isolates of Korea, Japan and China were classified as China + Korea, and China + Korea + Japan by phylogenetic analysis for RSV RNA1 and RNA2. In case of RNA3 involved in pathogenicity, genetic relationship of RSV among the three countries was grouped into 3 as China, China + Korea, and Korea + Japan. According to the genetic relationships in RSV RNA4, RSV isolates were grouped into 4 as China, Korea, China + Korea + Japan, and Korea + Japan. Viruliferous insect rate (VIR) of RSV in average increased in each year from 2008 to 2010, and the rates were 4.3%, 6.1%, and 7.2%, respectively, at the 28 major rice production areas in 7 provinces including Gyeonggido. The highest VIR in each year was 11.3% of Gyeonggido in 2008, 20.1% of Jellanamdo in 2009 and 14.2% of Chungcheongbukdo in 2010. The highest VIR depending upon the investigated areas was 22.1% at Buan of Jellabukdo in 2008, 36% at Wando and Jindo of Jellanamdo in 2009, and 30.0% at Boeun of Chungcheongbukdo in 2010. Average population density (APD) of overwintered SBPH was 13.1 heads in 2008, 13.9 heads in 2009 and 5.6 heads in 2010. The highest APD was 39.1 and 60.4 heads at Buan of Jellabukdo in 2008 and 2009, respectively, and 14.0 heads at Pyungtaeg of Gyeonggido. The acreage of RSV occurred fields was 869 ha in the western and southern parts, mainly at Jindo and Wando areas, of Jellanamdo in 2008. In 2009, RSV occurred in the acreage of 21,541 ha covered whole country, especially, partial and whole plant death were occurred with infection rate of 55.2% at 3,025 plots in 53 Li, 39 Eup/Myun, 19 Si/Gun of Gyeonggido, Incheonsi, Chungcheongnamdo, Jeollabukdo and Jeollanamdo. Seasonal development of overwintered SBPH was investigated at Buan, Jeollabukdo, and Jindo, Jeollanamdo for 3 years from 2008. Most SBPH developed to the 3rd and 4th instar on the periods of May 20 to June 10, and they developed to the adult stage for the 1st generation on Mid and Late June. In 2009, all SBPH trapped by sky net trap were adult on May 31 to June 1 at Mid-western aeas of Taean, Seosan and Buan, and South-western areas of Sinan and Jindo. The population density of adult SBPH was 963 heads at Taean, 919 at Seocheon and 819 at Sinan area. The origin of these higher population of adult SBPH were verified from the population of non-overwintered SBPH but immigrant SBPH. From Mid May to Mid June in 2010, adult SBPH could not be counted as immigrant insects by sky net trap. The variation of RSV VIR was high with 2.1% to 9.5% for immigrant adult SBPH trapped by sky net trap at Hongsung of Chungcheongbukdo, Buan of Jeollabukdo and so forth in 2009. The highest VIR for the immigrant adult SBPH was 9.5% at Boryung of Chungcheongnamdo, followed by 7.9% at Hongsung of Chungcheongnamdo, 6.5% at Younggwang of Jeollanamdo, and 6.4% at Taean of Cheongcheongnamdo. The infection rate of RSV on rice plants induced by the immigrant adult SBPH cultivated near sky net trap after about 10 days from immigration on June 12 in 2009 was 84.6% at Taean, 65.4% at Buan and 92.9% at Jindo, and 81% in average through genetic diagnosis of RT-PCR. Barley known as a overwintering host plant of RSV had very low infection rate of 0.2% from 530 specimens collected at 10 areas covering whole country including Pyungtaeg of Gyeonggido. Twenty nine plant species were newly recorded as natural hosts of RSV. In winter annual plant species, 11 plants including Vulpia myuros showed RSV infection rate of 24.9%. The plant species in summer annual ecotype were 13 including Digitaria ciliaris with 44.9%, Echinochloa crusgalli var. echinata with 95.2% and Setaria faberi with 65.5% in infection rate of RSV. Five perennial plants including Miscanths sacchariflorus with infection rate of 33.3% were recorded as hosts of RSV. Rice cultivars, 8 susceptible cultivars including Donggin1 and 17 resistant ones including Samgwang, were screened in field conditions at 3 different areas of Buan, Iksan and Ginje in 2009. All the susceptible cultivars were showed typical symptom of mosaic and wilt. In 17 genetic resistant cultivar, 12 cultivars were susceptible, however, 5 cultivars were field-resistant plus genetic resistant to RSV as non symptom expression. When RSV was artificially inoculated at seedling stage to 4 cultivars known as genetic resistant and 3 cultivars known as genetic susceptible, the symptom expression in resistant cultivars was lower as 19.3% in average than that of 53.3% in susceptible ones. In comparison of symptom expression rate and viral infection rate using resistant Nampyung and susceptible Heugnam cultivars by artificial inoculation of RSV at seedling stage, the symptom expression of Heugnam was higher as 28% than 12% of Nampyung. However, virion infection of resistant Nampyung cultivar was higher as 12% reversely than 85% of susceptible Heugnam. Yield loss of rice was investigated by the artificial inoculation of RSV at the seedling stage of resistant cultivars of Nampyung and Onnuri, and susceptible cultivars of Donggin1 and Ungwang for 3 years from 2008. The average yield per plant was 7.8 g, 8.5 g and 13.8 g on rice plants inoculated at seedling stage, tillering stage and maximum tillering stage, respectively. The yield loss rate was increased by earlier infection of RSV with 51% at seedling stage, 46% at tillering stage and 13% at maximum tillering stage. In resistant rice cultivars, there was no statistically significant relation between infection time and yield loss. In natural fields on susceptible rice cultivar of Ungwang at Taean and Jindo areas in 2009, the yield loss rate was increased with same tendency to the infection hill rate having the corelation coefficient of 0.94 when the viral infection was over 23.4%.
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