• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.1-8
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• 2014

Cloud computing helps big data processing to make various information using IT resources. The government has to start the RPS(Renewable Portfolio Standard) and induce the production of electricity using renewable energy equipment. And the government manages system to gather big data that is distributed geographically. The companies can purchase the REC(Renewable Energy Certificate) to other electricity generation companies to fill shortage among their duty from the system. Because of the RPS use voluntary competitive market in REC trade and the prices have the large variation, RPS is necessary to predict the equitable REC price using RPS big data. This paper proposed REC price prediction method base on fuzzy logic using the price trend and trading condition infra in REC market, that is modeled in cloud computing environment. Cloud computing helps to analyze correlation and variables that act on REC price within RPS big data and the analysis can be predict REC price by simulation. Fuzzy logic presents balanced REC average trading prices using the trading quantity and price. The model presents REC average trading price using the trading quantity and price and the method helps induce well-converged price in the long run in cloud computing environment.

• Journal of Korea Game Society
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• v.11 no.2
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• pp.45-56
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• 2011

The concept, means, value chain, the persons concerned and possible issues of game item trading in cash are analyzed in this study. The trading is characterized by not only being performed with its own unique business model but also causing issues related to delicate social problems, such as digital contents distribution, work place and gambling. The way of game item trading in cash and its value chain have been changed with game market growth for the past years. I analyzed in this article the new concept of game item trading in cash that is different from the past. Also, analysis on several issues related to game item trading in cash is conducted.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.5
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• pp.179-185
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• 2016

This study verifies whether speculative trading in commodity markets acted as the primary cause of the increase in commodity prices after the global financial crisis using the Structural Vector Autoregressive (SVAR) model. The effects of speculative trading on commodity prices increased by a factor of 3 to 6 after the crisis compared to those before the crisis. Although the demand related variables, such as industrial production, affected commodity prices significantly before the crisis, their effects decreased after the crisis. Consequently, the rebound of commodity prices after the crisis was mainly caused by the increase in speculative money, fortified by the expansion of the global liquidity supply. The global liquidity may well increase in the future, because the U.S. Federal Reserve Board is likely to continue to increase its interest rate. This study claims that when global liquidity shrinks as a result of a change in the Fed's monetary policy stance, speculative trading will slow down, leading to a decline in commodity prices.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.4
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• pp.735-742
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• 2021

Recently, renewable energy-related power generation facilities have been surging due to the government's "Renewable Energy 3020", "Green New Deal", "2050 Carbon Neutrality" and "K-RE100" policies. Most renewable energy facilities are small and distributed, making it difficult to manage efficiently, and small distributed resources less than 1MW are having a hard time with participating in the market due to the limited sales and avoidance of trading. In particular, the intermittency of renewable energy has a significant impact on the stability of the power grid. The government is seeking to address volatility and intermittency issues through 'small distributed resource brokerage trading, and to expand the systematic resourceization and acceptability of heterogeneous large and small distributed resources. In this work, we intend to apply an AI-based power generation prediction model to a distributed resource brokerage trading system so that it can be utilized as a foundation platform for pioneering new energy business markets.

• Environmental and Resource Economics Review
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• v.32 no.3
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• pp.149-166
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• 2023

The emissions trading system, introduced to reduce greenhouse gas emissions, experienced a sharp increase in emission allowance prices during the second plan period (2018-2020), which led to an increase in the demand for smooth supply and demand of emission allowances, while suppliers anticipating a shortage of emission allowances in the future did not participate in trading. Therefore, the authority temporarily revised the guidelines to ensure that the amount of allowances carried forward is proportional to the trading volume as a market stabilization measure. Through an optimization process using a dynamic nonlinear mathematical model, this paper analyzes the impact of the government's intervention on the carryover policy on GHG emission reductions and emission allowance market prices. According to the simulation analysis results, banking regulations could cause a decline in prices during the regulation period, even though the initial policy was predicted to be adopted.

• Smart Media Journal
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• v.11 no.1
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• pp.38-45
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• 2022

A lot of research is being going until this day in order to obtain stable profit in the stock market. Trading algorithms are widely used, accounting for over 80% of the trading volume of the US stock market. Despite a lot of research, there is no trading algorithm that always shows good performance. In other words, there is no guarantee that an algorithm that performed well in the past will perform well in the future. The reason is that there are many factors that affect the stock price and there are uncertainties about the future. Therefore, in this paper, we propose a model using TimeGAN that predicts future returns well and selects algorithms that are expected to have high returns based on past records of the returns of algorithms. We use TimeGAN becasue it is probabilistic, whereas LSTM method predicts future time series data is deterministic. The advantage of TimeGAN probabilistic prediction is that it can reflect uncertainty about the future. As an experimental result, the method proposed in this paper achieves a high return with little volatility and shows superior results compared to many comparison algorithms.

• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.53
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• pp.233-264
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• 2012

This study proposes and verifies an integrated model that reflects and helps to explain complexities and comprehensiveness of e-Trade in practice. The primary objective was to widen and deepen our understanding about the phenomena of paperless trading which will enable us to formulate an effective means to promote the adoption and utilization of e-Trade infrastructure. This investigation combined and extended the theories and models of TAM, TOE framework, and TTF in order to identify and hypothesize the causal relationships between factors of technology, organization and environment, and the usage level and performance of e-Trade. The previous research on e-Trade was reviewed and incorporated into the extended model. The integrated model was tested based on the survey data collected from a sample of 417 trading firms. Overall, the regression and path analysis demonstrated the usefulness of the proposed model as a conceptual framework for better understanding and explanation of e-Trade. The result of the analysis implies the relevancy of the factors of technology, organization and environment for facilitating usage of and increasing performance of e-Trade. However, rejection of a few hypothesized relationships implies the necessity of extensive promotion and education about the related laws and regulations, and education, supervision and incentives for e-Trade practitioners.

• The Journal of Asian Finance, Economics and Business
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• v.5 no.4
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• pp.95-100
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• 2018

The study investigates the factors that affect Kyrgyzstan's bilateral trade flows with its main trading partners and attempts to predict trade potential for Kyrgyzstan. Using panel data, the gravity model is applied to estimate Kyrgyzstan's trade from 2000 to 2016 for its 35 main trading partners. The coefficients derived from the gravity-model estimation are then used to predict trade potential for Kyrgyzstan. Results proved to be successful and explained 63% of the fluctuations in Kyrgyzstan's trade. According to the results, Kyrgyzstan's and its partners' GDP have a positive effect on trade, while distance and partners' population prove to have a negative effect. Predicted trade potential reveals that neighboring countries (China, Kazakhstan, Uzbekistan, and Tajikistan) and Russia still have a significant trade potential. Kyrgyzstan, being a less developed economy, even by Central Asia standards, can only achieve its goals of reducing poverty and becoming more developed by increasing its overall trade with the rest of the world. Therefore, it is essential to study the main determinants of Kyrgyzstan's bilateral trade. In this way, we can help policy makers formulate policies to expand Kyrgyzstan's trade. This study is the first attempt to apply to the gravity model to Kyrgyzstan in an attempt to predict trade potential.

• International Commerce and Information Review
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• v.12 no.2
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• pp.29-57
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• 2010

Global e-Trade system which electronically processes all trade transactions of trading companies is an essential platform where export and import firms enhance international competitiveness. This study is to develop research models suitable for e-Trade and empirically analyze them in order to examine and verify factors affecting e-Trade system of trading companies on the basis of previously-verified studies on global e-Trade such as TAM, TOE, and Task-Technology Fit Model(TTF). Among many factors, this paper comprehensively analyzes an acceptance factor, which is one of the factors affecting the implementation and performance of e-Trade, from the technological, organizational, and environmental context. The finding from this paper will be applied to e-Trade projects carried out by the government in the future by analyzing the correlation between acceptance and performance.

• Journal of Korean Society for Quality Management
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• v.28 no.1
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• pp.1-12
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• 2000

Since Black Monday there has been a rash of systems developments which aimed at automating and upgrading the surveillance mechanism of monitoring the many facets of security trading. A more sophisticated mathematical model for detecting abnormal trading activities was created by Davis and Ord of Penn State along with Nobel prize laureates Solow and Modigliani of MIT. They used CAPM(Capital Asset Pricing Model) to explain the movements of stock price and applied an idea of residuals to detect unusual movements. In this paper, their idea is discussed and a new method is proposed, which involves a confidence interval of future observation in linear regression. One of the examples of the stock watch system adopting this statistical method is also presented.