• Journal of Korea Technology Innovation Society
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• v.8 no.spc1
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• pp.297-322
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• 2005

The paper aims to analyze the logic and power of open source software (OSS), and to show the ways in which Korean government and companies support developing countries in the field of software development. Many developing countries are considering software industry to be a strategic industry due to the fact that software industry seems to be labor-intensive, or rather knowledge-intensive industry. In this regard, developing countries without huge financial investment can achieve certain level of economic development while leveraging software industry. Concerning software development tools, among recent trends OSS has been regarded as a viable alternative software development tool for developing countries. In developing countries, OSS is believed to resolve some difficulties caused by proprietary software such as Microsoft Windows, which is too expensive to buy for users and developers in low-income developing countries. In this sense, OSS has been considered as only solution for software developing because OSS is able to reduce the cost of software development and to enhance the technological capabilities of developing countries. In addition to the benefit of low cost, we have to shed light on the business model of OSS that is not to sell software licence, but to provide technical support and services. In order to use OSS as much as they can, developing countries have to invest for educating human resources who can develop and implement software system using OSS. These OSS-related policies can lead developing countries to developed countries.

• Journal of the Korean Society of Costume
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• v.59 no.7
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• pp.17-33
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• 2009

The purpose of this study was to get some pieces of prior information to eventually develop high value-added fashion cultural products design using local cultural resources, to promote the cultural tourism festival. For this purpose, this study is carried out the investigation of cultural product stores and visitors' questionnaire survey. The subjects of this study were festivals which were selected as cultural tourism festival by the Ministry of Culture, Sports and Tourism from 2000 to 2008. Of them, six festivals were finally selected but food festival was excluded. The results of this study were as follows; First, the store survey was conduced to analyze the situation of the products of cultural tourism festival. The most frequent product was accessories. And a T-shirt was found to be sold every festival probably because it was the most popular item and basic item which people could buy without burden. While the most diverse kinds of products were found in the Andong, the Jinju and Gangjin were found not to develop various products. In the design motif used for cultural products, most products did not use festival or local image. The highest use of the festival and local image was found in Gangjin and Muju. The Andong and Chungju were found to sell very common products buying anywhere rather than products using local cultural resources or image. In the material of cultural products, most products use metal. And In the price of cultural products, 10,000-30,000 won was found highest. Second, the purchase conditions of cultural tourism festival visitors were examined. The visiting goal and companion of visitors was found to vary with the type of cultural tourism festival. The types of visitors were also found to have an effect on the choice of items in the purchase of cultural products sold in the festival. Only one third of respondents responded buying one and more cultural products. The purchase rate was found high in the festival where cultural product items were various and there were many products symbolizing festival or region. The most purchased item was a mobile phone hanger and the amount of purchasing cultural products was 10,000-30,000 won. The reason not to purchase cultural products was dissatisfaction with utility, originality, possibility of a present, symbolism, and price. The most important attribute in the purchase of cultural products was design, followed by symbolism, price, originality, and innovation. The highly preferred product group included clothing, miscellaneous goods, and accessories. Specifically, T-shirt was found highest. Based on these research results, it was found that the design strategy for the cultural products development should consider both regional and festival images. The items and designs of the cultural products should reflect visitors' characteristics and the price zone should be varied.

• The Korean Journal of Financial Management
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• v.26 no.4
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• pp.165-189
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• 2009

According to disclosure regulation, insider can hide their trading until disclosure day, because there be interval between trading time and disclosure time. To accommodate strategic trade, they have an incentive to be brought disclosure interval as long as possible. This research investigate whether strategical behaviour of informed traders using disclosure intervals exists in domestic stock market.ls xt, we aney he whether they can get abnormal return through stealth strategy after announcement date. We also evaluate the effect of mimicking trading on price impact with the assumption of existence of mimicking trading. Our major research results are as follows: In case of main shareholder without having no prompt disclosure duty, the frequency of trading started at the beginning of month is shown significantly higher than others. This result shows a direct evidence that informed traders buy or sell their equity strategically using disclosure intervals. Also, we find the result that the coefficient of strategic variables has highest value in middle size information. However, the empirical evidence that informed trader get abnormal return through strategic trading was not shown in this study. Meanwhile, stock price over-reacts for selling transaction on trading point and is recovered after disclosure date., so we assume possibility of mimicking trading exists in domestic stock market.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.157-177
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• 2022

Investors are trading stocks by keeping a close watch on the order information submitted by domestic and foreign investors in real time through Limit Order Book information, so-called price current provided by securities firms. Will order information released in the Limit Order Book be useful in stock price prediction? This study analyzes whether it is significant as a predictor of future stock price up or down when order imbalances appear as investors' buying and selling orders are concentrated to one side during intra-day trading time. Using classification algorithms, this study improved the prediction accuracy of the order imbalance information on the short-term price up and down trend, that is the closing price up and down of the day. Day trading strategies are proposed using the predicted price trends of the classification algorithms and the trading performances are analyzed through empirical analysis. The 5-minute KOSPI200 Index Futures data were analyzed for 4,564 days from January 19, 2004 to June 30, 2022. The results of the empirical analysis are as follows. First, order imbalance information has a significant impact on the current stock prices. Second, the order imbalance information observed in the early morning has a significant forecasting power on the price trends from the early morning to the market closing time. Third, the Support Vector Machines algorithm showed the highest prediction accuracy on the day's closing price trends using the order imbalance information at 54.1%. Fourth, the order imbalance information measured at an early time of day had higher prediction accuracy than the order imbalance information measured at a later time of day. Fifth, the trading performances of the day trading strategies using the prediction results of the classification algorithms on the price up and down trends were higher than that of the benchmark trading strategy. Sixth, except for the K-Nearest Neighbor algorithm, all investment performances using the classification algorithms showed average higher total profits than that of the benchmark strategy. Seventh, the trading performances using the predictive results of the Logical Regression, Random Forest, Support Vector Machines, and XGBoost algorithms showed higher results than the benchmark strategy in the Sharpe Ratio, which evaluates both profitability and risk. This study has an academic difference from existing studies in that it documented the economic value of the total buy & sell order volume information among the Limit Order Book information. The empirical results of this study are also valuable to the market participants from a trading perspective. In future studies, it is necessary to improve the performance of the trading strategy using more accurate price prediction results by expanding to deep learning models which are actively being studied for predicting stock prices recently.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.77-97
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• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.