• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• Journal of the Korea Society of Computer and Information
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• v.7 no.2
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• pp.196-204
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• 2002

This paper examines the price discovery role of the KOSPI 200 futures index for its cash index. It was used the intrady data for KOSPI 200 and futures index from July 1998 to June 2001. The existing Preceding study for KOSPI 200 futures index was used the data of early market installation, but this study is distinguished to use a recent data accompanied with the great volume of transaction and various investors. We established three hypothesis to examine whether there is the price discovery role in the KOPSI 200 futures index and the characteristics of that. First, to examine whether the lead-lag relation is induced by the infrequent trading of component stocks, observations are sorted by the size of the trading volume of cash index. In a low trading volume, the long lead time is reported and the short lead time in a high volume. It is explained that the infrequent trading effect have an influence on the price discovery role. Second, to examine whether the lead-lag relation is different under bad news and good news, observations are sorted by the sign and size of cash index returns. In a bad news the long lead time is reported and the short lead time in a good news. This is explained by the restriction of"short selling" of the cash index Third, we compared estimates of the lead and lag relationships on the expiration day with those on days prior to expiration using a minute-to-minute data. The futures-to-spot lead time on the expiration day was at least as long as other days Prior to expiration, suggesting that "expiration day effects" did not demonstrate a temporal character substantially different form earlier days. Thus, while arbitrage activity may be presumed to be the greatest at expiration, such arbitrage transactions were not sufficiently strong or Pervasive to alter the empirical price relationship for the entire day. for the entire day.

• Journal of Arbitration Studies
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• v.20 no.1
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• pp.87-107
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• 2010

This paper reviews the distinctive characteristics between e-Trade and e-Commerce in view of commercial arbitration in Korea and explores several improvements for the role of commercial arbitration. As the volume of e-Trade and e-Commerce has expanded day by day, there will be more disputes between traders no matter where the commerce may occur. But despite increasing of the disputes relating to e-Commerce transaction, it seems that the role of commercial arbitration has been shrunk instead. Korea needs to improve the role of commercial arbitration in order to meet and lead the age of u-Trade Hub(u-TH) service and to adopt an offensive or active attitude when arbitration used. Moreover, it is suggested that the competence of arbitration should not only be intensified more precisely but also be redesigned more systematically. Korea should take advantage of arbitration resources actively such as arbitrators as human resource and experiences as knowledge assets and also prepare the policy for sharing those arbitration resources between arbitrators more effectively.

• Journal of the Korea Safety Management & Science
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• v.11 no.4
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• pp.221-228
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• 2009

Electronic Product Code Information Services(EPCIS) is an EPCglobal standard for sharing EPC related information between trading partners. EPCIS provides a new important capability to improve efficiency, security, and visibility in the global supply chain. EPCIS data are classified into two categories, master data (static data) and event data (dynamic data). Master data are static and constant for objects, for example, the name and code of product and the manufacturer, etc. Event data refer to things that happen dynamically with the passing of time, for example, the date of manufacture, the period and the route of circulation, the date of storage in warehouse, etc. There are four kinds of event data which are Object Event data, Aggregation Event data, Quantity Event data, and Transaction Event data. This thesis we propose an event-based data model for EPC Information Service repository in RFID based integrated logistics center. This data model can reduce the data volume and handle well all kinds of entity relationships. From the point of aspect of data quantity, we propose a formula model that can explain how many EPCIS events data are created per one business activity. Using this formula model, we can estimate the size of EPCIS events data of RFID based integrated logistics center for a one day under the assumed scenario.

• 한국경영정보학회:학술대회논문집
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• 2007.06a
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• pp.661-666
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• 2007

MMORPG, massively multi-players in online role-playing game, is the most popular genre in online games. Because large number of players interacts with each other, virtual worlds in MMORPG are alike communities of real worlds. Moreover, players in virtual worlds trade game items with real money. This paper is to find impacts of real-money trading into real worlds, and game operators, by using two-period model between players of the game and the game operator. It was found that real-money trading benefits game operators, and there exists optimal supply of game items to maximize the profit of game operator. Moreover we found that the income disparity in real worlds could be decreased when real-money trading is allowed To support the analytical model, we used an empirical analysis using real-money trading data, and find the relationship among play time of MMORPG, transaction volume of real-money trading, and price of game items. In empirical analysis, it was found that real-money trading benefits game operators. Moreover, it was found that play time and price have positive relationship.

• Knowledge Management Research
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• v.9 no.3
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• pp.59-76
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• 2008

Due to rapid increase in number of Internet users, the volume of domestic Internet shopping is expanding dramatically. Although the Internet shopping mall business is steadily growing, it has structural weakness, and in general, the business does not generate profit. In this context, this research identifies characteristics of the Internet shopping non-adopters, and suggests strategies to strengthen competitiveness of domestic Internet shopping malls and to create a new demand. Analysis of MCR data in 2007 showed that out of 4,298 respondents, 2,206 people adopted Internet shopping (51.3%). and 2,092 did not (48.7%). The survey measured 28 items regarding a consumption pattern and a lifestyle of the Internet users, and the analysis result showed that the pattern can be categorized in seven groups. Based on the analysis, the research suggests that the domestic Internet shopping malls adopt strategies to increase consumers' access frequency to tile Internet service, provide high-end goods, diversify transaction methods, make online shopping more convenient, cater to diversified consumer demands based on demographic data, provide price comparison more Internet shopping mails, and provide sufficient and useful information to consumers.

• Journal of Information Technology Services
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• v.17 no.2
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• pp.207-218
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• 2018

Recently, it becomes a big trend in the banking industry to apply a big data analytics technique to extract essential knowledge from their customer database. Such a trend is based on the capability to analyze the big data with powerful analytics software and recognize the value of big data analysis results. However, there exits still a need for more systematic theory and mechanism about how to adopt a big data analytics approach in the banking industry. Especially, there is no study proposing a practical case study in which big data analytics is successfully accomplished from the marketing perspective. Therefore, this study aims to analyze a target marketing case in the banking industry from the view of big data analytics. Target database is a big data in which about 3.5 million customers and their transaction records have been stored for 3 years. Practical implications are derived from the marketing perspective. We address detailed processes and related field test results. It proved critical for the big data analysts to consider a sense of Veracity and Value, in addition to traditional Big Data's 3V (Volume, Velocity, and Variety), so that more significant business meanings may be extracted from the big data results.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.4B no.2
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• pp.47-53
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• 2004

The salient pole rotor type single phase SRM (switched reluctance motor) uses radial and axial direction magnetic flux simultaneously. Therefore, the output power per unit volume is very high and the shaft length is shorter than other types of SRMs with the same output. Furthermore, it can be manufactured with low cost owing to its simple structure and driving circuit. The prototype was designed using the theory of the traditional rotating machine and 3D FEM analysis. On this paper, the experiment apparatus, which includes the fabricated prototype in previous researches, was fabricated to measure the current and voltage of the prototype. Then the flux linkage, inductance and magnetic co-energy were calculated using the experimental results. Finally, the measured magnetic co-energy was compared with the simulated magnetic co-energy.

• Asia pacific journal of information systems
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• v.12 no.3
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• pp.115-133
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• 2002

The past research have suggested that the Inter-Organizational E-Commerce based on the Inter-Organizational Systems(IOSs) has direct impact on the Inter-Organizational Relationships(IORs) in general. Considering the importance of this issue, however, few empirical research have been conducted. The objective of this study is to examine the impact of the level of Inter-Organizational E-Commerce on the performance of IORs in particular. Reviewing the literature, we suggested a research model and developed ten hypotheses to be tested. We conducted a survey and collected data from 125 companies which were using. EDI systems. The data were analyzed using LISREL 8.30. The major findings are: (1) The volume of E-Commerce has a positive impact on the commitment of trading partners. (2) High quality of information sharing and high degree of standardization improve the trust and the commitment between trading parties. (3) The trust and the commitment are critical precedent variables of cooperation. (4) The more partners cooperate each other, the more transaction cost will be reduced and as a result, the more financial performance will improve. A discussion on the result of the hypotheses test is followed by a discussion on the academic and practical implications of this study.

• Korean Journal of Agricultural Science
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• v.47 no.2
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• pp.283-290
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• 2020

In 2018, 647 thousand tons of eggs were produced and consumed. However, the issue of pesticides used for egg in 2017 made Korean consumers worry about the food safety of eggs, and the volume of egg consumption decreased. The Korean egg industry also has another problem due to an unclear and inefficient marketing structure at the farm level. This marketing situation of eggs at the farm level in Korea needs a large-scale restructuring of the market structure, including introducing an EPC (egg processing center). Especially, the introduction of an EPC has been discussed by government officers and specialists, but the social benefit of an EPC, which will be the driving point for approving an EPC, has not been measured yet. The purpose of this study was to measure the effect of introducing an EPC in Korea. Through an analysis using EDM (equilibrium displacement model), a few findings are presented. First, the introduction of an EPC may increase the transparency of price discovery and decrease the transaction cost. And thus, it results in a higher producer price, lower consumer price, and larger quantity at market equilibrium. Second, an EPC will improve the level of food safety of eggs, which can increase the satisfaction of domestic producers and consumers. Third, the introduction of an EPC may create new consumption of eggs. Based on these three effects, the new social benefits in monetary terms from the introduction of an EPC in Korea could be 23.9 - 35.2 billion won.