• The Journal of the Korea Contents Association
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• v.19 no.10
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• pp.45-58
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• 2019

As modern people's daily lives are becoming more harsh in Korea, the so-called Kidults generation has appeared since a few years ago as adults have come back to their childhood sensibility and are exposed to various cultures online, and there are many people who have a hobby for collecting toys. However, as there is currently no formalized system for individual toy trade online, it is difficult to acquire expertise and share information with each other through a major portal site's $caf{\acute{e}}$, and is exposed to security or fraud while trading toys. In this paper, we design and implementation of a web-based toy trading system. Analyzing the advantages and disadvantages of the various trading and relay systems currently in use, it will provide opportunities for professional toy knowledge and information exchange to many users who have a hobby of collecting toys, and will greatly help vitalize the toy market through a secure and convenient trading environment between individuals.

• Information Systems Review
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• v.18 no.2
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• pp.23-38
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• 2016

Previous studies on information systems (IS) and finance suggest that information on stock message boards influence the investment decisions of individual investors. However, how information on online stock message boards influences an individual investor's buy or sell decisions is unclear. To address this research question, we investigate the relationship between a number of posts on stock message boards and order imbalance in stock markets. Order imbalance is defined as the difference between the daily sum of buy-side shares traded and the daily sum of sell-side shares traded. Therefore, order imbalance can suggest the direction of trades and the strength of the direction with trading volumes. In this regard, this study examines how the number of posts (information on stock message boards) influences order imbalance (stock trading behavior). We collected about 46,077 messages of 40 companies on the Korea Composite Stock Price Index from Paxnet, the most popular Korean online stock message board. The messages we collected were divided based on in-trading and after-trading hours to examine the relationship between the numbers of posts and trading volumes. We also collected order imbalance data on individual investors. We then integrated the balanced panel data sets and analyzed them through vector regression. We found that the number of posts on online stock message boards is positively related to prior order imbalance. We believe that our findings contribute to knowledge in IS and finance. Furthermore, this study suggests that investors should carefully monitor information on stock message boards to understand stock market sentiments.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.6
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• pp.267-276
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• 2022

The objective of this study was to examine the relationship between knowledge management and business performance through the role of innovation capabilities in door manufacturing companies in Ho Chi Minh City, Vietnam. The study proved the importance of knowledge management as well as the important role of innovation capacity in affecting the business performance of door manufacturing companies. The study was conducted by surveying 400 managers/CEOs who are members of the Board of Directors who directly run door manufacturing and trading businesses in Ho Chi Minh City collected from March 2021 to October 2021. The authors used confirmatory factor analysis (CFA) to determine the most common observed variables of each factor. Research findings indicated that knowledge management orientation and innovation capability impacted business performance and confirmed the mediating role of innovation capability towards previous variables. These results kindly contribute to theoretical and practical bricks of building determinants of business performance as well as knowledge management indoor manufacturers for future consideration. From the above results, the study has suggested managerial implications to further improve the investment in developing knowledge management elements and innovation capacity to achieve high business results in enterprises door production in Ho Chi Minh City, Vietnam in the future.

• Knowledge Management Research
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• v.11 no.5
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• pp.79-90
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• 2010

As the volume of South Korean trading increased, the pressure from the international community on South Korea to perform export control system for strategic items (ECS) increased as well. However, the South Korean government has been giving a tepid response toward the ECS, due to lack of the knowledge on psychological reactance of firms. This paper investigates the structural relationships between environmental factors and the attitude toward the ECS, and the adoption of the ECS. The author discuss the implications of the findings in this article which are useful for the government to find strategic policy direction.

• Journal of the Korea Society of Computer and Information
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• v.15 no.1
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• pp.207-217
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• 2010

It is taken a violent fall of the international stocks market that was an American Subprime Mortgage Situation. The loss rate of individual investor judged than foreigner and institution by bigger thing. Therefore, further scientific and mechanical investment is needed at the stock investment using Internet HTS. This dissertation is stocks profit rate analysis which uses individual engine foreigner Knowledge Base HTS at the Bear Period the Bear Wave Period the Bull Period the Bull Wave Period. Knowledge Based e-friend HTS was Installed. HTS does composite stock exchange index in actuality stock trading and engine's fund earning rate, yield that is abroad comparative analysis using trend line that is HTS tool, MACD, Bollinger Bands, Stochastic slow's function. Usually, each subjects suppose that deal 5 stocks, and comparative study of the profit(loss)rate of the down to earth falling rate and rising rate, by comparing the earning rate of 5 Small capital stocks with 5 medium capital stocks and 5 Large capital stocks during the bear period, the bear wave period, the bull period, the bull wave period has meaning at the making research of the financial IT field.

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

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

• Asia pacific journal of information systems
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• v.20 no.3
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• pp.33-49
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• 2010

We empirically examine the impact of online auction knowledge and category-specific knowledge on the final price of online auctions. Specifically, we question how the relationship between buying and selling experiences affects the final prices of online auctions. Related to the trust between buyers and sellers, we examine the multiple interactions between a buyer-seller pairand aim to identify how these repeated transactions influence the final price. To contrast these effects with other product related factors, we focus on so called 'common value' auctions of vintage stamps on eBay, in which the ex-post value of the product is the same among participating agents’ perceived value. Online auction of stamps provides a representative setting to examine the relationship between market experience and the auction participation behavior in the common value auction, as it provides the book value of stamp as well as price variation across individual buyers with different expertise levels. Our analysis of over 3000 stamps auctions on eBay indicates a significantly high frequency of buyer-seller (pair) interactions, thus suggesting a 'relationship view' of auctions. The work validates five hypotheses derived from the existing theory in economics, marketing, and information systems. Through the common-value auction data, we find that seller's online auction experience and category-specific experience favor sellers by increasing the final price. However, buyer's online auction experience does not affect the final price, but buyer's category-specific experience favors buyers by decreasing the final price. We find that the trust between two trading parties increases the final price.

• Knowledge Management Research
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• v.11 no.4
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• pp.67-77
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• 2010

MICE (Meetings, Incentive travels, Conventions, and Exhibitions) segment is one of the fastest growing segments of the tourism industry today. Especially, the government promotes the convention business as a strategic industry given its growing importance as a high value-added export. The push-pull factor conceptual frameworks were used to identify motives that lead firms to attend conventions. Furthermore, the authors assess firms' service quality, preference, reliability, and positional advantage of Korea Electronics Show(KES). Using data from a number of Korean trading firms, the authors find considerable results and conclude by discussing recommendation for the convention industry.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.8 no.1
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• pp.61-68
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• 2013

As modern society changes toward knowledge based society, the patent policy and professional manpower need to be changed because interest and importance about patent, trademarks, intellectual property right and copyright of business secret are increasing. In order to facilitate trading of the technology developed in the private sector and to promote the business, the Act of technology transfer and commercialization promotion is prepared. In the law, the article 14 says that who have expertise on commercialization of the technology transfer can be registered as a technology trader to the Minister of Knowledge Economy. For the purpose of finding improvements of the technology trader's registration system, comparison method was studied. Technology trader compare with licensed real estate agent which is similar with it in terms of trade. There are several results from this study by followings. The unique tasks of technology traders should be specified for increasing authority of technology transfer expert. Manual criteria of post management should be prepared through registration certificate management agency which operated by charging. In addition, The announcement document should be prepared carefully for necessity of announcement and registration criteria of technology trading business. These improvements are enable to motivate trading market and impact to expand the base of technology marketing and technology transfer-commercialization.

• 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.