• Journal of Korea Port Economic Association
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• v.23 no.4
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• pp.159-175
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• 2007

The purpose of this study is to examine the causal relationship between trading volume of freight and industrial growth in Korea ports, and to induce policy implications. In order to test whether time series data is stationary and the model is fitness or not, we put in operation unit root test, cointegration test. And we apply Granger causality based on an error correction model, Hsiao(1981) method and variance decomposition. The results indicate that the extent of causality between trading volume of freight and industrial growth is strong in order of Incheon port, Busan port, Gwang Yang port, Ulsan port. We can infer policy suggestions as follows; The port policy of government must be focused on re-adjusting investment among Korea ports and raising competitive power of Korea ports

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1471-1479
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• 2017

The variability of trade price index of apartment influences on the various aspect, especially economics, social phenomenon, industry, and culture of the country. In this article, the autoregressive error (ARE) model has been considered for analyzing the monthly trading price index of apartment data. About 16 years of the monthly data have been used from September 2001 to May 2017. In the ARE model, six macroeconomic variables are used as the explanatory variables for the rade price index of apartment. The six explanatory variables are mortgage rate, oil import price index, consumer price index, KOSPI stock index, GDP, and GNI. The result has shown that trading price index of apartment explained about 76% by the mortgage rate, and KOSPI stock index.

• International journal of advanced smart convergence
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• v.11 no.1
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• pp.19-27
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• 2022

Across the world, 'housing' comprises a significant portion of wealth and assets. For this reason, fluctuations in real estate prices are highly sensitive issues to individual households. In Korea, housing prices have steadily increased over the years, and thus many Koreans view the real estate market as an effective channel for their investments. However, if one purchases a real estate property for the purpose of investing, then there are several risks involved when prices begin to fluctuate. The purpose of this study is to design a real estate price 'return rate' prediction model to help mitigate the risks involved with real estate investments and promote reasonable real estate purchases. Various approaches are explored to develop a model capable of predicting real estate prices based on an understanding of the immovability of the real estate market. This study employs the LSTM method, which is based on artificial intelligence and deep learning, to predict real estate prices and validate the model. LSTM networks are based on recurrent neural networks (RNN) but add cell states (which act as a type of conveyer belt) to the hidden states. LSTM networks are able to obtain cell states and hidden states in a recursive manner. Data on the actual trading prices of apartments in autonomous districts between January 2006 and December 2019 are collected from the Actual Trading Price Disclosure System of the Ministry of Land, Infrastructure and Transport (MOLIT). Additionally, basic data on apartments and commercial buildings are collected from the Public Data Portal and Seoul Metropolitan Government's data portal. The collected actual trading price data are scaled to monthly average trading amounts, and each data entry is pre-processed according to address to produce 168 data entries. An LSTM model for return rate prediction is prepared based on a time series dataset where the training period is set as April 2015~August 2017 (29 months), the validation period is set as September 2017~September 2018 (13 months), and the test period is set as December 2018~December 2019 (13 months). The results of the return rate prediction study are as follows. First, the model achieved a prediction similarity level of almost 76%. After collecting time series data and preparing the final prediction model, it was confirmed that 76% of models could be achieved. All in all, the results demonstrate the reliability of the LSTM-based model for return rate prediction.

• Proceedings of the Korea Association of Information Systems Conference
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• 2003.05a
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• pp.55-73
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• 2003

As the development of wireless technologies continue, mobile stock trading has become a new channel for companies to reach their customers. This study examines the factors affecting customer acceptance of mobile stock trading services. The research model based on the previous studies was established and the research hypotheses were generated. The sample was divided into users and nonuser groups. The test results show that relative advantage and social influence are positively related to intention to accept mobile services as well as in intention to reuse, security risk is negatively related to intention to reuse, frequency of trading is positively related to intention to reuse, cost burden is negatively related to the probability of using mobile stock trading services, and social influence is positively related to the probability of using mobile services. The results also support that information quality and response time are positively related to relative advantage, and interface quality is negatively related to complexity. It is considered that the study results may help managers to increase customer use of mobile stock trading services.

• Korean Business Review
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• v.18 no.1
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• pp.43-57
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• 2005

This paper investigate to test the validity of the basic assumptions of strategic trading models with the broker and informed trader using daily closing data of KOSPI 200 stock index futures for the year 2001-2003. Major results are summarized as follows: (i) For these years, while foreign investors and brokerage companies traded for the directions consistent with the model, brokerage companies and individual investors traded for inconsistent directions. (ii) Cross correlation function (CCF) analysis shows no systematic dependency in the trading between all three participants(foreign investor, brokerage companies and individual investors) for these years. (iii) Chi-square validity test for the 30 days of the largest unexpected trading volume shows some systematic dependency in the trading between three participants for these years. Finally, some limitations of this paper and direction for further research were suggested.

• Journal of the Korea Society of Computer and Information
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• v.12 no.4
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• pp.257-267
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• 2007

Bandwidth Trading(BT) represents a potential market with over 1 trillion USD across the world and high growth potential. BT is also likely to accelerate globalization of the telecommunications industry and massive restructuring driven by unbundling rush. However, systematic researches on BT remain at an infant stage. This study starts with structure analysis of the Internet industry, and discusses significance of Internet interconnection with respect to BT issues. We also describe the bandwidth commoditization trends and review technical requirements for effective Internet interconnection with BT capability. Taking a step further, this study explores the possibility of improving efficiency of network providers and increasing user convenience by developing an architectural prototype of Hub-&-Spoke interconnection model required to facilitate BT. The BT market provides an innovative base to ease rigidity of two-party contract and increase service efficiency. However, as fair, efficient operation by third party is required, this research finally proposes an exchanging hub named NIBX(New Internet Business eXchange).

• Journal of Broadcast Engineering
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• v.25 no.6
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• pp.922-934
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• 2020

The talent trading platform is a platform that brokers transactions such as program coding, media content production (video, music, presentation materials, etc.), design, learning, and repair. Existing talent trading platforms provide a server-client model-based service, which incurs server operating costs and arbitration labor costs for transactions, which has a disadvantage that users bear high service fees. This paper proposes a method to reduce server and database operation costs by uploading transaction information to blocks through the system as a distributed app (dApp) based on the Ethereum platform. In addition, it proposes a method to lower transaction fees by reducing the labor cost of transaction arbitrators through smart contracts. Compare and analyze the cost processing procedure and transaction fee size of the blockchain-based talent trading platform and the existing talent trading platform.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.2
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• pp.143-161
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• 2022

This study examines the effect of used trading app's consumption value and protection motivation and the perceived usefulness and continuous use intention. The proposed research model and developed hypotheses were tested using structural equations modeling based on data collected from 293 customers with experience in used transaction app's. The results of the study confirm the positive effects of the used trading app's consumption value and protection motive theory is perceived usefulness of customer. In addition, there is a positive relationship between a customer's perceived usefulness and continuous use intention of used trading app's. The study provides On a theoretical level valuable insights into the sustainability of transaction app's after post-COVID 19 and the importance of developing used trading app's consumption value and protection motivation, but there is also a limitation that the region is limited.

• Environmental and Resource Economics Review
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• v.33 no.2
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• pp.179-201
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• 2024

In introducing emissions trading schemes, many countries start with a high level of free allocation to reduce the sudden cost burden on companies and increase acceptance of the policy. The free allocation is then gradually reduced, considering the risks of carbon leakage. This aligns with the "polluter pays" principle and is often considered one of the elements of an advanced emissions trading scheme. In this context, this study uses a simple emissions trading market model to show that decreasing the free allocation rate may not be desirable if the emissions market is not perfectly competitive. In particular, by identifying the existence of a free allocation rate at which the cost inefficiency is minimized, this study demonstrates that having a low level of free allocation does not necessarily imply the improvement of the emissions trading scheme.

• Transactions of the Korean hydrogen and new energy society
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• v.33 no.6
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• pp.692-698
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• 2022

With the expansion of domestic hydrogen fuelling station infrastructure, it is necessary to secure reliability among hydrogen traders, and for this, technology to accurately measure hydrogen is important. In this study, 4 types of hydrogen trading volume calculation models (model 1-4) were presented to improve the accuracy of the hydrogen trading volume. In order to obtain the reference value of model 4, and experiment was conducted using a flow rate measurement equipment, and the error rate of the calculated value for each model was compared and analyzed. As a result, model 1 had the lowest metering accuracy, model 2 had the second highest metering accuracy and model 3 had the highest metering accuracy until a certain point. But after the point, model 2 had the highest metering accuracy and model 3 had the second metering accuracy.