• Korean Journal of Construction Engineering and Management
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• v.15 no.5
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• pp.147-159
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• 2014

After 2008 global financial crisis, Korean housing market has experienced stagnation. So it caused housing market problems like housing price reduction, rising rent cost and so on. For housing market normalization government announced policies but Korean housing market didn't recover from stagnation. So, to understand why Korean housing market couldn't overcome the recession and why the policies didn't be effective, this research analyzed housing market participants (home owner, housing demand) based on the law of supply and demand and the psychological effect on their transaction intention based on behavioral economics(behavioral finance). Based on the analysis this research tested the effectiveness of announced policies using System Dynamics. The result showed that the amount of transaction and mortgage loan was influenced by the length of time to draft policies.

• Asia pacific journal of information systems
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• v.29 no.4
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• pp.673-689
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• 2019

The Internet enables businesses to acquire a great deal of information, including prices in the open markets. In this study, we investigate what the value of reference price information is to a company in the market and how the company can make use of such information. Using business analytics, we were able to estimate prices of used cars for a rent-a-car company. The results show that a smart pricing information system is useful for collecting online reference price information and for estimating future prices of used cars and rental prices.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.8
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• pp.379-384
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• 2002

In the competitive electricity market, various pricing methods are developed and practiced in many countries. Among these pricing methods, marginal loss factor(MLF) can be applied to reflect the marginal cost of network losses. For the calculation of MLF, power flow method has been used to calculate system loss deviation. However, this power flow method shows some shortcomings such as necessity of regional reference node, and absence of an ability to consider network constraints like line congestion, voltage limit, and generation output limit. The former defect might affects adversely to the equity of market participants and the latter might generate an inappropriate price signals to customers and generators. To overcome these defects, the utilization of optimal power flow(OPF) is suggested to get the system loss deviation in this paper. 30-bus system is used for the case study to compare the MLF results by the power flow and the OPF method for 24-hour dispatching and pricing, Generator payment and customer charge are compared with these two methods also. The results show that MLF by OPF reflects the power system condition more faithfully than that of by the conventional power flow method

• 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.

• Korean Journal of Agricultural Science
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• v.20 no.2
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• pp.211-220
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• 1993

Sustainable agriculture is a management system for ecological equilibrium and long-run productivity. Conversions from conventional to sustainable farming systems could have good effects on future generations' productivity and agricultural market opening in Korea. However, farmers are not willing to adopt sustainable farming system as because of farm income reductions, so government programs may be needed. Government programs should have research support for cost reductions, direct support for advocational livestock breedings and income security, and relative price changes for reducing agricultural chemicals.

• The Journal of the Korea Contents Association
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• v.10 no.5
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• pp.229-240
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• 2010

Most of all researchs and analyses in the field of television industry of Korea were related to Cable Television SO(System Operator), but few about PP(Program Provider) from the viewpoint of Industrial Theory in Korea. However, there wasn't a comprehensive research analysis in terms of the co-relationship of the market structure, market conduct and market performance of PP. This research analyzes co-relationship and dynamics of the market structure of PP, its market conduct and market performance in a comprehensive way in Cable TV industry. Especially this paper focuses on the analysis of 1)relationship and its influence between market structure and market conduct, 2) relationship and its influence between their market conducts and market performances and 3) relationship and its influence between market structure and market performance among 40 commercial PPs in terms of the theory of Industrial Organization in Korea. This paper is delated and reported as follows in conclusion : 1)the type of horizontal integration has an effect on the price and scale in the relationship between the structure and its conduct. 2)the price has effect on the revenue and viewing rate between the conduct and performance. And high dependency of the Cable TV license fee has an effect on viewing rate and revenue per subscribers(ARPU) between the conduct and performance. 3)The horizontal integration between the structure and performance had a positive effect on viewing rate and its product differentiation has an effect on the revenue per subscribers. Net cost of the product had a negative effect on the rate of profit.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.10a
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• pp.185-188
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• 2000

Dongdaemoon clothes market can make traditional commerce to improve problems which happen to traditional commerce and satisfy between customers and salers through combination among Electronic Commerce which is growing up in 21 centuries place innovation This thesis is focusing on suggesting strategies which practice to let Dongdaemoon clothes market customers and wholesalers, retailers to use Electronic Commerce as strategic skills by analyzing core successful factors for adopting Electronic Commerce in Dongdaemoon clothes market. Adopting Electronic Commerce in Dongdaemoon clothes market when the customer make a reservation and the salers provide the customer with discounting service, the customer was willing to but it. Internet service categories which affect customer's satisfaction are providing lots of product information. This thesis shows providing information made the customer to increase customers's satisfaction degree and buying intention. Also convenience of product research, and reliability in transaction process can enable the customer to increase transaction reliability. These factors are very important in Electronic Commerce. In addition, factors which show customer's suggestion and inconvenience by using best seller information and discounting service board when they buy some items in Dongdaemoon clothes market affect the customer satisfaction degree and satisfaction degree of providing information. However, this thesis is analyzed that reliability of transaction process doesn't affect all successful factors such as product quality, size, online payment system, price reliability.

• The Journal of Information Systems
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• v.29 no.3
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• pp.119-144
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• 2020

Purpose Real estate usually takes charge of the highest proportion of physical properties which individual, organizations, and government hold and instability of real estate market affects the economic condition seriously for each economic subject. Consequently, practices for predicting the real estate market have attention for various reasons, such as financial investment, administrative convenience, and wealth management. Additionally, development of machine learning algorithms and computing hardware enhances the expectation for more precise and useful prediction models in real estate market. Design/methodology/approach In response to the demand, this paper aims to provide a framework for forecasting the real estate market with machine learning algorithms. The framework consists of demonstrating the prediction efficiency of each machine learning algorithm, interpreting the interior feature effects of prediction model with a state-of-art algorithm, LIME(Local Interpretable Model-agnostic Explanation), and comparing the results in different cities. Findings This research could not only enhance the academic base for information system and real estate fields, but also resolve information asymmetry on real estate market among economic subjects. This research revealed that macroeconomic indicators, real estate-related indicators, and Google Trends search indexes can predict real-estate prices quite well.

• Journal of Distribution Research
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• v.15 no.5
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• pp.257-272
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• 2010

The DongDeaMoon fashion market was carried out the core role of low price distribution as production and selling illuviation of fashion merchandise. However, the unique and successful fashion merchandising system of DongDeaMoon fashion market has been seriously threatened by consumer need changes as well as by an increased competition from new types of retail stores such as fast fashion brands and online shopping malls. Therefore, this study reviewed the policies for DongDeaMoon fashion market carried out by Seoul Metropolitan City and formulated the policies for strengthening competitiveness of DongDeaMoon fashion market.

• The Journal of Bigdata
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• v.3 no.1
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• pp.13-20
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• 2018

Stock market prediction has been long dream for researchers as well as the public. Forecasting ever-changing stock market, though, proved a Herculean task. This study proposes a novel stock market sentiment lexicon acquisition system that can predict the growth (or decline) of stock market index, based on economic news. For this purpose, we have collected 3-year's economic news from January 2015 to December 2017 and adopted Word2Vec model to consider the context of words. To evaluate the result, we performed sentiment analysis to collected news data with the automated constructed lexicon and compared with closings of the KOSPI (Korea Composite Stock Price Index), the South Korean stock market index based on economic news.