• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2012.05a
• /
• pp.422-425
• /
• 2012

Korea's conventional iPhone users had to pay for their applications in dollars. However, Apple will have to consider implementing a payment system in Korean won. Apple has strongly insisted on using the payment system in dollars, but despite its relatively small sales proportion, it will turn to the Korean market that ranks 3rd in the world for its number of downloads. In other words, the Korean market has tremendous potential for growth. Thus, this study aimed to analyze the changes that will be experienced by Apple in the Korean market due to the implementation of the iPhone-based won payment system, price fluctuations due to the introduction of iTunes, as well as positive aspects and implications of Apple's market expansion in Korea when users make payments in Korean won.

• The Journal of Society for e-Business Studies
• /
• v.4 no.3
• /
• pp.63-76
• /
• 1999

Internet-based electronic trade has been growing fast. But most users are not yet familiar with the system and find it very difficult to purchase and sell the products in the cyber market place. To handle these problems, agent-based virtual market place system has been proposed where agents instead of individuals participate in trading of goods. Most of the proposed models have been in the two general categories. The first is the direct transaction among sellers and buyers, and the second is the agent-based transaction. However, the transaction is not fair and the best deal can't be guaranteed for both models. In this paper, we propose a new broker based synchronous transaction algorithm which is fair to both parties and guarantees the best deal. Our algorithm is implemented using Visual C++ and the experimental results show that our method is better than the two traditional transaction models in every performance metrics, Number of transactions are increased up to 21% and price adjustment is up to 280% better for some transactions.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.51 no.7
• /
• pp.311-316
• /
• 2002

It has been a basic model for the present electric power industry that more than two generators compete, and thereby the market clearing price and the generation schedules are determined through the bid process. In order for this paradigm to be applicable to real electric power systems and markets, it is necessary to reflect many physical and economic constraints related to frequency and transmission in the dispatching schedule. The paper presents an approach to deriving a Nash bargaining solution in a competitive electricity market where multiple generators are playing with the system operator who mitigates the transmission congestion to minimize the total transaction cost. In this study, we take the effect of the line flows and the role of system operator into the Game. Finally, a case study has been demonstrated to verify the proposed cooperative game.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2003.05a
• /
• pp.329-337
• /
• 2003

Stock market prediction is regarded as a challenging task of financial time-series prediction. There have been many studies using artificial neural networks in this area. Recently, support vector machines (SVMs) are regarded as promising methods for the prediction of financial time-series because they me a risk function consisting the empirical ewer and a regularized term which is derived from the structural risk minimization principle. In this study, I apply SVM to predicting the Korea Composite Stock Price Index (KOSPI). In addition, this study examines the feasibility of applying SVM in financial forecasting by comparing it with back-propagation neural networks and case-based reasoning. The experimental results show that SVM provides a promising alternative to stock market prediction.

• The Pure and Applied Mathematics
• /
• v.16 no.4
• /
• pp.427-441
• /
• 2009

The method of Lattice Dynamical System is used to establish a global model on an infinite chain of many local markets interacting each other through a diffusion of prices between them. This global model extends the Walrasian evolutionary cobweb model in an independent single local market to the global market evolution. We assume that each local market has linear decreasing demands and quadratic supplies with naive predictors, and investigate the stationary behaviors of global price dynamics and show that their dynamics are conjugate to those of $H{\acute{e}}non$ maps and hence can exhibit complicated behaviors such as period-doubling bifurcations, chaos, and homoclic orbits etc.

• Journal of the Korea Convergence Society
• /
• v.8 no.3
• /
• pp.215-223
• /
• 2017

The purpose of the present study is to identify the differences of customer preference in online and offline used car business. Conjoint analysis is used for examining the attributes of used car choices. As a result, the order of importance in real used car market is as follows: brand, design, price, model, mileage. Whereas the order of importance in online used car market is as follows: brand, trust, price, web design, accident. Accordingly, the author suggested that customer preferences depend on the path people are approaching and the attributes of preference vary in online and real stores. For example, trust and accident are important attributes in online market in comparison with real market. Used car market is increasing and becoming important. The authorities and practitioners need to understand used car market and establish the related policies.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.42 no.2
• /
• pp.1-8
• /
• 2019

Network externalities are essentially dynamic in that the value consumers feel about a product is affected by the size of the existing customer base that uses that product. However, existing studies on network externalities analyzed the effects of network externalities in a static way, not dynamic. In this study, unlike previous studies, the impact of network externalities on price competition in a vertically differentiated market is dynamically analyzed. To this end, a two-period duopoly game model was used to reflect the dynamic aspects of network externalities. Based on the game model, the Nash equilibria for price, sales volume, and revenue were derived and numerically analyzed. The results can be summarized as follows. First, if high-end product has strong market power, the high-end product vendor takes almost all benefits of the network externality. Second, when high-end product has strong market power, the low-end product will take over most of the initial sales volume increase. Third, when market power of high-end product is not strong, it can be seen that the effects of network externalities on the high and low-end products are generally proportional to the difference in quality. Lastly, if there exists a strong network externality, it is shown that the presence of low-end product can be more profitable for high-end product vendor. In other words, high-end product vendor has incentive to disclose some technologies for the market entrance of low-end product, even if it has exclusive rights to the technologies. In that case, however, it is shown that the difference in quality should be maintained significantly.

• Journal of Intelligence and Information Systems
• /
• v.25 no.3
• /
• pp.1-17
• /
• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.

• Journal of Digital Contents Society
• /
• v.16 no.5
• /
• pp.767-773
• /
• 2015

This study suggests a reliable auction right analysis system that improves the existing auction right analysis in a sense that the system is more applicable to a real economic world. The existing study has used an auction successful bidding price rate and an auction successful contract price for its estimation of the auction right analysis. But in this paper the degree of market vitality which is provided from the commercial zone analysis is used as an input variable so the expected auction price can be estimated and the auction right analysis is conducted in that manner.

• Korean Journal of Agricultural Science
• /
• v.32 no.1
• /
• pp.95-106
• /
• 2005

This study was focused on analyzing the impacts of e-commerce on the farmer's management behavior and suggesting alternatives for the development of e-commerce in agricultural industry. For this study, survey was conducted for 24 farmers who sell agricultural products through e-commerce in Chungnam province. The results of study are as follows; First, farmers have changed their management practices in terms of production, marketing and processing by using the information of consumers' preferences while doing e-commerce business. Second, farmers have attempted to differentiate their product through product brand and customer relationship marketing, because they recognized the importance of developing marketing techniques adapted to e-commerce system for more revenues. Third, if quality certification system of agricultural products is introduced under e-commerce, farmers would use it for their environmentally sounded farming because they expect to increase their income. Fourth, 75% of the farmers sold their product at retail price. It means that e-commerce farmers act as a price maker rather than price taker at e-commerce market, who will be encouraged to have larger business size resulting in more added value. Based on the results of study, we suggest that there should be reduction of service charge for credit card, and encouragement of B2B transaction for the economy of scale and introduction of quality certification system so as to establish e-commerce system of agricultural industry as soon as possible.