• The Journal of Asian Finance, Economics and Business
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• v.7 no.8
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• pp.51-59
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• 2020

The objective of this study is to enrich the literature by investigating the impact of introduction of index future trading on spot market returns and trading volume in Vietnam. Data used in this study mainly consist of daily VN30-Index and market trading volume series during the period from February 6^th, 2012 to December 31^st, 2019. Using OLS, GARCH(1,1) and EGARCH(1,1) models, the empirical findings consistently confirm that the introduction of index future trading has no impact on the spot market returns. In addition, the results of the EGARCH(1,1) model indicate that the leverage effect on the spot market volatility is existence in HOSE. Specifically, bad news has a greater effect on the market volatility than good news of the same size. Moreover, our empirical findings reveal that the introduction of index future contracts has the positive impact on the underlying market trading volume. Specifically, the trading volume of the post-index futures introduction increases by 7.5 percent compared with the pre-index futures introduction. Finally, the results obtained from the Granger causality test for the relationship between the spot market returns and the future trading activity confirm that only uni-directional causality running from the market returns to the future trading activity exists in HOSE.

• Journal of applied mathematics & informatics
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• v.5 no.1
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• pp.133-133
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• 1998

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.377-396
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• 2023

As the cryptocurrency market continues to grow, it has developed into a new financial market. The need for investment strategy research on the cryptocurrency market is also emerging. This study aims to conduct an empirical analysis on an investment methodology of cryptocurrency that combines short-term trading strategy and deep learning. Daily price data of the Ethereum was collected through the API of Upbit, the Korean cryptocurrency exchange. The investment performance of the experimental model was analyzed by finding the optimal parameters based on past data. The experimental model is a volatility breakout strategy(VBS), a Long Short Term Memory(LSTM) model, moving average cross strategy and a combined model. VBS is a short-term trading strategy that buys when volatility rises significantly on a daily basis and sells at the closing price of the day. LSTM is suitable for time series data among deep learning models, and the predicted closing price obtained through the prediction model was applied to the simple trading rule. The moving average cross strategy determines whether to buy or sell when the moving average crosses. The combined model is a trading rule made by using derived variables of the VBS and LSTM model using AND/OR for the buy conditions. The result shows that combined model is better investment performance than the single model. This study has academic significance in that it goes beyond simple deep learning-based cryptocurrency price prediction and improves investment performance by combining deep learning and short-term trading strategies, and has practical significance in that it shows the applicability in actual investment.

• Environmental and Resource Economics Review
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• v.10 no.4
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• pp.547-567
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• 2001

This paper analyzed what kind of institutional scheme for domestic policy instruments to reduce GHG emissions are desirable for Korea in complying with the international efforts to mitigate climate change, by focusing on independent abatement(equivalent to the imposition of carbon tax) and domestic emission trading. It also examined the economic and environmental implications of recycling the government revenue created from implementation of those policies. By utilizing a dynamic CGE model, this study shows that the economic cost under independent abatement is projected to be higher than that under emission trading. It is because under independent abatement scheme each emitter in economy must meet its emission target regardless of the abatement cost. On the other hand, emission trading allows emitters to reduce the marginal cost of abatement through trading of emission permits. In designing future domestic policies and measure to address the climate change problem in Korea, therefore, this study proposes the introduction of domestic emission trading scheme as the main domestic policy instrument for GHG emission abatement. In terms of double dividend, in addition, this study shows that both independent abatement and emission trading schemes under various assumption on the revenue recycling may not generate the double dividend in Korea.

• The Journal of Information Technology
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• v.6 no.1
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• pp.85-105
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• 2003

The study revealed that the companies are still reluctant to stop using document-based payment methods although they are aware the use of electronic payment system would reduce express and save time. Considering problems that are suggested in the conventional payment method, more companies will introduce and utilize the electronic payment system. Moreover, as the refined electronic payment system is essential in electronic trade, active studies on this field would greatly contribute to the development of electronic payment system. The prevailing settlement of international trade still relies on trading documents while the legal issues are not finalized for electronic bill of lading (B/L) and electronic bill. This research purpose on comparing some e-trading models in character and presenting the obstacles of e-trading activation and the solutions. e-trading models this research has studied are BOLERO, TradeCard, electronic L/C etc. Comparing characteristic points of the e-trading model in this article are as follows; 1)Access ways as a global e-trading model, 2)Structural and Functional characteristic, 3)Role as a global e-trading model, 5)Legal and Application issues in practice, 6)Security issues for Technology. But Further studies on this subject would be needed.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.8
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• pp.131-141
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• 2020

This study is designed to investigate the impact of China exchange rate policy on its trading partners by using a country multi-dataset GVAR model. Our model includes samples of 30 countries, six from high-income, six from middle-income and eighteen from low-income countries. This study used annual time series data over the period 1992 to 2017. We constructed currency misalignment index and it provided some interesting features about the currency undervaluation and overvaluation. The results of the currency misalignment shows that China's Renminbi is structurally more undervalued over the sample period as compared to other countries, and fluctuation in major currencies effects the global trade around the world. The overall empirical results of the GVAR model indicate that RMB undervaluation affects the trade pattern and macroeconomic performance of China's trading partners. Overall, China's exchange rate undervaluation has mixed effects on trading partner's GDP, exports and imports. The devaluation of China's RMB efficiently stimulated China's exports and reduced imports. While, in some countries, this effect is reverse, the RMB undervaluation increases the GDP of partner countries and also increases their exports to China. The results confirm the strong and leading role of the Chinese Renminbi in the global trade.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1199-1215
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• 2011

Relaxing an unrealistic assumption of a representative percolation model, this paper demonstrates that herd behavior leads to a high increase in volatility but not trading volume, in contrast with information flows that give rise to increases in both volatility and trading volume. Although detecting herd behavior has posed a great challenge due to its empirical difficulty, this paper proposes a new methodology for detecting trading days with herding. Furthermore, this paper suggests a herd-behavior-stochastic-volatility model, which accounts for herding in financial markets. Strong evidence in favor of the model specification over the standard stochastic volatility model is based on empirical application with high frequency data in the Korean equity market, strongly supporting the intuition that herd behavior causes excess volatility. In addition, this research indicates that strong persistence in volatility, which is a prevalent feature in financial markets, is likely attributed to herd behavior rather than news.

• Smart Media Journal
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• v.11 no.3
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• pp.9-17
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• 2022

Algorithmic trading, which uses algorithms to trade financial products, has a problem in that the results are not stable due to many factors in the market. To alleviate this problem, ensemble techniques that combine trading algorithms have been proposed. However, there are several problems with this ensemble method. First, the trading algorithm may not be selected so as to satisfy the minimum performance requirement (more than random) of the algorithm included in the ensemble, which is a necessary requirement of the ensemble. Second, there is no guarantee that an ensemble model that performed well in the past will perform well in the future. In order to solve these problems, a method for selecting trading algorithms included in the ensemble model is proposed as follows. Based on past data, we measure the contribution of the trading algorithms included in the ensemble models with high performance. However, for contributions based only on this historical data, since there are not enough past data and the uncertainty of the past data is not reflected, the contribution distribution is approximated using the Dirichlet distribution, and the contribution values are sampled from the contribution distribution to reflect the uncertainty. Based on the contribution distribution of the trading algorithm obtained from the past data, the Transformer is trained to predict the future contribution. Trading algorithms with high predicted future contribution are selected and included in the ensemble model. Through experiments, it was proved that the proposed ensemble method showed superior performance compared to the existing ensemble methods.

• Journal of the Korean Operations Research and Management Science Society
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• v.40 no.1
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• pp.21-33
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• 2015

This paper develops an analytic model for minimizing the cost of distributing items by truck from one supplier to many customers under Milk run logistics strategy. The model derives formulas for not only inventory and transportation costs but also costs associated with carbon emission trading scheme. In addition, monetary investment for reducing carbon emissions is considered. We analyze how to determine optimal shipment size and carbon emission reduction investment. The purpose of this work is to evaluate the effects of carbon emission trading scheme on the Milk run logistics strategy in terms of how much to reduce carbon emissions and/or inventory and transportation costs. We analytically show that it is possible to reduce carbon emissions while reducing inventory and transportation costs by introducing cap-and-trade carbon emission trading scheme under certain conditions.

• International Commerce and Information Review
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• v.4 no.2
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• pp.97-119
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• 2002

This research purposes on comparing some e-trailing models in character and presenting the obstacles of e-trailing activation and the solutions, e-trailing models this research has studied are 1) Bolero, 2) TradeCard, 3) BeXcom, 4) Identrus, and 5) TEDI etc. Comparing characteristic points of the e-trading models are as follows ; 1) Access ways as a global e-trading model, 2) Structural characteristic, 3) Functional characteristic, 4) Role as a global e-trading model, 5) Legal issues, 6) Application issues in practice, 7) Security issues for Technology.