• The Journal of the Korea Contents Association
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• v.18 no.11
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• pp.277-284
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• 2018

The purpose of this study was to investigate the Q - method, which is one of the qualitative analysis methods to approach deep and intrinsic meaning about the perception of college students about food in traditional market. Recently, local governments have been developing diverse tourism products aimed at young people in order to revitalize traditional markets. In this study, we classify the perception of food in traditional market by university students, And to suggest strategic implications by using it as basic data for establishing marketing strategy so that young people can visit in the futures. In order to analyze the perception of college students' subjective perception of traditional market food, Factor analysis was used to conduct an exploratory study. To do this, a Q-sort, Program, and Q factor analysis. The results were classified into three types. The first type (N = 21): Memories seeking type, the second type (N = 6): Local culture resource seeking type, the third type(N = 5). Each of these subjective perceptions can be used as a basis for future research. Through the establishment of marketing strategies for each of the three types of classifications, the direction of traditional markets is presented, and a variety of food items that are valuable as local tourism resources are accommodated by accepting university students' to contribute to the revitalization of traditional markets.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.52-56
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• 2004

The Information of classification and estimate about KOSPI200 index`s up and down in the stock market becomes an important standard of decision-making in designing portofolio in futures and option market. Because the coming trend of time series patterns, an economic indicator, is very subordinate to the most recent economic pattern, it is necessary to study the recent patterns most preferentially. This paper compares classification and estimated performance of SVM(Support Vector Machine) and Fuzzy SVM model that are getting into the spotlight in time series analyses, neural net models and various fields. Specially, it proves that Fuzzy SVM is superior by presenting the most suitable dimension to fuzzy membership function that has time series attribute in accordance with learning Data Base.

• Environmental and Resource Economics Review
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• v.30 no.4
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• pp.535-555
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• 2021

This study empirically analyzes the effects of international oil prices on domestic stock market volatility. The data used for the analysis are 10-minute high-frequency data of the KOSPI index and WTI futures price from January 2, 2015, to July 30, 2021. For using the high-frequency data, a heterogeneous autoregression (HAR) model is employed. The analysis model utilizes the advantages of high frequency data to observe the impact of international oil prices through realized volatility, realized skewness, and kurtosis as well as oil price return. In the estimation, the Box-Cox transformation is applied in consideration of the distribution of realized volatility with high skewness. As a result, it finds that the daily return fluctuation of the WTI price has a statistically significant positive (+) effect on the volatility of the KOSPI return. However, the volatility, skewness, and kurtosis of the WTI return do not appear to affect the volatility of the KOSPI return. This result is believed to be because the volatility of the KOSPI return reflects the daily change in the WTI return, but does not reflect the intraday trading behavior of investors.

• Archives of design research
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• v.13 no.4
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• pp.85-93
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• 2000

Design becomes a source of new competitive power in the boundless global market so-called globalization. The competitive power in business was lied in the technology in 70's and the design was understood as a styling or graphic means. However, the design has become more important means to get the competitive power in business since 1980. World businesses have found the fact that it has a super competitive power to make the product's performance as well as its dignity rather than it is to determine the product's external view or color. The change of design policy in Phillips, one of the world's leading producers of electronic products, is not much different. Design manager's power was limited until 70's. However, Phillips has focused its business strategy on the higher competitive power since 1980 and they welcomed Robert Blaich, vice president of design and development at Herman Miller Inc., to be a member of the company, expanding the importance of design along with restructure while working on the globalization. Meanwhile, Stefano Marzano, a Senior Director in 90's, established a high design concept, working on the strategic futures to get customer-oriented and for successful commercialization. The vision of the future developed over 3 years until 1996 was to forecast 10 years coming up and create a new value while achieving the business target through the design as an innovative design in bracing for the information network era.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.195-220
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• 2018

In this study, we conducted an empirical analysis of the factors that affect the change of Bitcoin Closing Price. Previous studies have focused on the security of the block chain system, the economic ripple effects caused by the cryptocurrency, legal implications and the acceptance to consumer about cryptocurrency. In various area, cryptocurrency was studied and many researcher and people including government, regardless of country, try to utilize cryptocurrency and applicate to its technology. Despite of rapid and dramatic change of cryptocurrencies' price and growth of its effects, empirical study of the factors affecting the price change of cryptocurrency was lack. There were only a few limited studies, business reports and short working paper. Therefore, it is necessary to determine what factors effect on the change of closing Bitcoin price. For analysis, hypotheses were constructed from three dimensions of consumer, industry, and macroeconomics for analysis, and time series data were collected for variables of each dimension. Consumer variables consist of search traffic of Bitcoin, search traffic of bitcoin ban, search traffic of ransomware and search traffic of war. Industry variables were composed GPU vendors' stock price and memory vendors' stock price. Macro-economy variables were contemplated such as U.S. dollar index futures, FOMC policy interest rates, WTI crude oil price. Using above variables, we did times series regression analysis to find relationship between those variables and change of Bitcoin Closing Price. Before the regression analysis to confirm the relationship between change of Bitcoin Closing Price and the other variables, we performed the Unit-root test to verifying the stationary of time series data to avoid spurious regression. Then, using a stationary data, we did the regression analysis. As a result of the analysis, we found that the change of Bitcoin Closing Price has negative effects with search traffic of 'Bitcoin Ban' and US dollar index futures, while change of GPU vendors' stock price and change of WTI crude oil price showed positive effects. In case of 'Bitcoin Ban', it is directly determining the maintenance or abolition of Bitcoin trade, that's why consumer reacted sensitively and effected on change of Bitcoin Closing Price. GPU is raw material of Bitcoin mining. Generally, increasing of companies' stock price means the growth of the sales of those companies' products and services. GPU's demands increases are indirectly reflected to the GPU vendors' stock price. Making an interpretation, a rise in prices of GPU has put a crimp on the mining of Bitcoin. Consequently, GPU vendors' stock price effects on change of Bitcoin Closing Price. And we confirmed U.S. dollar index futures moved in the opposite direction with change of Bitcoin Closing Price. It moved like Gold. Gold was considered as a safe asset to consumers and it means consumer think that Bitcoin is a safe asset. On the other hand, WTI oil price went Bitcoin Closing Price's way. It implies that Bitcoin are regarded to investment asset like raw materials market's product. The variables that were not significant in the analysis were search traffic of bitcoin, search traffic of ransomware, search traffic of war, memory vendor's stock price, FOMC policy interest rates. In search traffic of bitcoin, we judged that interest in Bitcoin did not lead to purchase of Bitcoin. It means search traffic of Bitcoin didn't reflect all of Bitcoin's demand. So, it implies there are some factors that regulate and mediate the Bitcoin purchase. In search traffic of ransomware, it is hard to say concern of ransomware determined the whole Bitcoin demand. Because only a few people damaged by ransomware and the percentage of hackers requiring Bitcoins was low. Also, its information security problem is events not continuous issues. Search traffic of war was not significant. Like stock market, generally it has negative in relation to war, but exceptional case like Gulf war, it moves stakeholders' profits and environment. We think that this is the same case. In memory vendor stock price, this is because memory vendors' flagship products were not VRAM which is essential for Bitcoin supply. In FOMC policy interest rates, when the interest rate is low, the surplus capital is invested in securities such as stocks. But Bitcoin' price fluctuation was large so it is not recognized as an attractive commodity to the consumers. In addition, unlike the stock market, Bitcoin doesn't have any safety policy such as Circuit breakers and Sidecar. Through this study, we verified what factors effect on change of Bitcoin Closing Price, and interpreted why such change happened. In addition, establishing the characteristics of Bitcoin as a safe asset and investment asset, we provide a guide how consumer, financial institution and government organization approach to the cryptocurrency. Moreover, corroborating the factors affecting change of Bitcoin Closing Price, researcher will get some clue and qualification which factors have to be considered in hereafter cryptocurrency study.

• Knowledge Management Research
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• v.21 no.3
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• pp.45-65
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• 2020

It is desirable to invest in wine that increases its value, but wine investment itself is unfamiliar in Korea. Also, the process itself is unreasonable, and information is often forged, because pricing in the wine market is done by a small number of people. With the right solution, however, the wine market can be a desirable investment destination in that the longer one invests, the higher one can expect. Also, it is expected that the domestic wine consumption market will expand through the steady increase in domestic wine imports. This study presents the consortium block chain framework for revitalizing the wine market and enhancing transparency as the "right solution" of the nation's wine investment market. Blockchain governance can compensate for the shortcomings of the wine market because it guarantees desirable decision-making rights and accountability. Because the data stored in the block chain can be checked by consumers, it reduces the likelihood of counterfeit wine appearing and complements the process of unreasonably priced. In addition, digitization of assets resolves low cash liquidity and saves money and time throughout the supply chain through smart contracts, lowering entry barriers to wine investment. In particular, if the governance of the block chain is composed of 'chateau-distributor-investor' through consortium blockchains, it can create a desirable wine market. The production process is stored in the block chain to secure production costs, set a reasonable launch price, and efficiently operate the distribution system by storing the distribution process in the block chain, and forecast the amount of orders for futures trading. Finally, investors make rational decisions by viewing all of these data. The study presented a new perspective on alternative investment in that ownership can be treated like a share. We also look forward to the simplification of food import procedures and the formation of trust within the wine industry by presenting a framework for wine-owned sales. In future studies, we would like to expand the framework to study the areas to be applied.

• The Korean Journal of Financial Management
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• v.17 no.1
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• pp.253-281
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• 2000

가격의 일일 등락폭을 상하 일정률로 제한하는 가격제한폭제도는 증거금 제도와 함께 증권시장의 양대 안정장치의 하나이다. KOSPI 200 현 선물시장에서도 가격제한폭 변경이 또KOSPI 200 도입이후 네 번 있었다. 따라서 연구는 가격제한폭의 변경 전후의 KOSPI 200 현 선물지수와 거래량 자료를 대상으로 수익률의 변동성 분석, 거래량 분석, 시장반응을 분석하였다. 본 연구의 실증적 연구결과는 다음과 같다. 첫째, 현물시장만 가격제한폭을 변경한 경우 변경 전후에는 현 선물시장의 수익률변동성에는 변화가 없는 것으로 나타났으나, 현 선물시장의 거래량 변동성 차이는 가격제한폭 변경후에 작은 것으로 분석되었다. VAR 분석에 의하면 변경후가 변경전에 비하여 선물이 현물을 선도하는 시차가 작아진 것으로 나타나 변경후가 더 효율적인 시장임을 알 수 있다. 둘째, 선물시장만 가격제한폭 일부의 제도를 변경한 경우 변경후에 현 선물시장의 수익률변동성과 거래량변동성이 축소된 것으로 나타나 안정적임을 알 수 있다. VAR 분석에 의하면 변경후가 변경전에 비하여 선물이 현물을 선도하는 시차가 작아진 것으로 나타나 변경후가 더 효율적인 시장임을 알 수 있다. 셋째, 현 선물시장이 동시에 제도를 변경한 경우 다음과 같다. 1998년 3월 2일의 경우 선물시장은 수익률 변동성 차이가 없는 것으로 나타났으나, 현물시장은 변경후 수익률의 변동성이 적은 것으로 나타났다. 거래량의 변동성은 현 선물시장에서 변경후가 작은 차이가 있는 것으로 분석되었다. VAR 분석에 의하면 변경후가 변경전에 비하여 선물이 현물을 선도하는 시차가 커진 것으로 나타나 현물시장과 선물시장이 동시에 가격제한폭 확대후에 비효율 적으로 되었다는 의미로 판단된다. 1998년 12월 7일의 경우 변경후에 현 선물시장에서는 수익률 및 거래량의 변동성이 작은 것으로 나타났다. 변경전에는 선물시장에 비해 현물시장의 수익률, 변동성이 높은 것으로 나타났으나, 변경후에는 현물시장에 비해 선물시장의 수익률 변동성이 높은 것으로 나타났다. VAR 분석에 의하면 변경후가 변경전에 비하여 선물이 현물을 선도하는 시차가 다소 커진 것으로 나타나 현물시장과 선물시장이 동시에 가격제한폭 확대후에 비효율적으로 되었다는 의미로 판단된다.

• Journal of Convergence for Information Technology
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• v.9 no.12
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• pp.133-140
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• 2019

This study examines the reason of a staggering trade deficit on the Korean agro-food industry. To achieve the goal of the study, this study suggests the policy implication for enlargement a trade deficit with foreign exchange rate. Despite the majority of grain importer does realize that there is a huge affection for price volatility on the business result, they are more likely to take flat pricing through the physical market to avoid risk of price volatility with exchange rate. Also the analysis of external and internal environments around the Korean agro-food export & import are conducted, particularly with the analysis of trade volume and food price affecting the export & import. Results from a survey show that the common factor to the effective use of overseas agricultural and foreign currency futures trading for grain traders in Korea.

• 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.