• Knowledge Management Research
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• v.22 no.2
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• pp.19-32
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• 2021

Bitcoin, a representative cryptocurrency, is receiving a lot of attention around the world, and the price of Bitcoin shows high volatility. High volatility is a risk factor for investors and causes social problems caused by reckless investment. Since the price of Bitcoin responds quickly to changes in the world environment, we propose to predict the price volatility of Bitcoin by utilizing news information that provides a variety of information in real-time. In other words, positive news stimulates investor sentiment and negative news weakens investor sentiment. Therefore, in this study, sentiment information of news and deep learning were applied to predict the change in Bitcoin yield. A single predictive model of logit, artificial neural network, SVM, and LSTM was built, and an integrated model was proposed as a method to improve predictive performance. As a result of comparing the performance of the prediction model built on the historical price information and the prediction model reflecting the sentiment information of the news, it was found that the integrated model based on the sentiment information of the news was the best. This study will be able to prevent reckless investment and provide useful information to investors to make wise investments through a predictive model.

• Journal of Korea Port Economic Association
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• v.38 no.4
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• pp.87-106
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• 2022

Uncertainties in the energy market are increasing due to technology developments (shale revolution), trade wars, COVID-19, and the Russia-Ukraine war. Especially, since 2020, the risk of international trade in the energy market has increased significantly due to changes in the supply chain of transportation and due to prolonged demand reduction because of COVID-19 and the Russian-Ukraine war. Considering these points, this study analyzed connectedness between energy price, tanker index, and uncertainty to understand the connectedness between international trade in the energy market. Main results are summarized as follows. First, as a result of analyzing stable period and unstable period of the energy price model using the MS-VAR model, it was confirmed that both the crude oil market model and the natural gas market model had a higher probability of maintaining stable period than unstable period, increasing volatility by specific events. Second, looking at the results of the analysis of the connectedness between stable period and unstable period of the energy market, it was confirmed that in the case of total connectedness, connectedness between variables was increased in the unstable period compared to the stable period. In the case of the energy market stable period, considering the degree of connectedness, it was confirmed that the effect of the tanker freight index, which represents the demand-side factor, was significant. Third, unstable period of the natural gas market model increases rapidly compared to the crude oil market model, indicating that the volatility spillover effect of the natural gas market is greater when uncertainties affecting energy prices increase compared to the crude oil market.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.582-583
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• 2021

As the production of new and renewable energy such as solar and wind power has diversified, microgrid systems that can simultaneously produce and consume have been introduced. . In general, a decrease in electricity prices through solar power is expected in summer, so producer protection is required. In this paper, we propose a transparent and safe gift power transaction system between users using blockchain in a microgrid environment. A futures is simply a contract in which the buyer is obligated to buy electricity or the seller is obliged to sell electricity at a fixed price and a predetermined futures price. This system proposes a futures trading algorithm that searches for futures prices and concludes power transactions with automated operations without user intervention by using a smart contract, a reliable executable code within the blockchain network. If a power producer thinks that the price during the peak production period (Hajj) is likely to decrease during production planning, it sells futures first in the futures market and buys back futures during the peak production period (Haj) to make a profit in the spot market. losses can be compensated. In addition, if there is a risk that the price of electricity will rise when a sales contract is concluded, a broker can compensate for a loss in the spot market by first buying futures in the futures market and liquidating futures when the sales contract is fulfilled.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.584-585
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• 2021

As the production of new and renewable energy such as solar and wind power has diversified, microgrid systems that can simultaneously produce and consume have been introduced. In general, a decrease in electricity prices through solar power is expected in summer, so producer protection is required. In this paper, we propose a transparent and safe gift power transaction system between users using blockchain in a microgrid environment. A futures is simply a contract in which the buyer is obligated to buy electricity or the seller is obliged to sell electricity at a fixed price and a predetermined futures price. This system proposes a futures trading algorithm that searches for futures prices and concludes power transactions with automated operations without user intervention by using a smart contract, a reliable executable code within the blockchain network. If a power producer thinks that the price during the peak production period is likely to decrease during production planning, it sells futures first in the futures market and buys back futures during the peak production period to make a profit in the spot market. losses can be compensated. In addition, if there is a risk that the price of electricity will rise when a sales contract is concluded, a broker can compensate for a loss in the spot market by first buying futures in the futures market and liquidating futures when the sales contract is fulfilled.

• Environmental and Resource Economics Review
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• v.33 no.1
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• pp.1-32
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• 2024

The emissions trading system stands as a pivotal climate policy in Korea, incentivizing abatement equivalent to 87% of total emissions (as of 2021). As the system likely has a far-reaching impact, it is crucial to understand how the real economic activity, energy sector, as well as environment would be influenced by its implementation. Employing a macroeconometric model, this paper is the first study analyzing the effects of the Korean emissions trading policy. It interconnects the Korean Standard Industrial Classification (Economy), Energy Balance (Energy), and National Inventory Report (Environment), enhancing its real-world explanatory power. We find that a 50% increase in emission permit price over four years results in a decrease in greenhouse gas emissions (-0.043%) and downward shifts in key macroeconomic variables, including real GDP (-0.058%), private consumption (-0.003%), and investment (-0.301%). The price increase in emission permit is deemed crucial for achieving greenhouse gas reduction targets. To mitigate transition risk associated with price shocks, revenue recycling using auction could ensure the sustainability of the economy. This study confirms the comparative advantage of expanded current transfers expenditure over corporate tax reduction, particularly from an economic growth perspective.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.6
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• pp.317-324
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• 2010

This paper analyses the performance of push-pull strategy by Multicharts' portpolio tool. This strategy decreases a risk of option sell by moving an exercise price to outward and compensates the profit by doubling contracts. This strategy also makes risk-free option sell to move inward for earning gain. This strategy basically uses several symbols. And this feature makes impossible to use a portpolio tool. However, This paper provides the method to use it with extracting intrinsic data from a symbolname. As an experiment for one month of september experiment options, it shows 1.5 million won in case of 5-displace and also shows 32 million won in case of 2.5-displace. If a Margin is enough, this strategy always earns profit, but it's difficult to be applicated in real trades because of a margin risk.

• Journal of the Korean Data and Information Science Society
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• v.22 no.1
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• pp.37-47
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• 2011

Stock price index option market has various investment strategies that have been developed. Specially, arbitrage strategies are very important to be efficient in option market. The purpose of this study is to improve profit using rough set and Box spread by using past option trading data. Option trading data was based on an actual stock exchange market tick data ranging from 2001 to 2006. Validation process was carried out by transferring the tick data into one-minute intervals. Box spread arbitrage strategies is low risk but low profit. It can be accomplished by back-testing of the existing strategy of the past data and by using rough set, which limit the time line of dealing. This study can make more stable profits with lower risk if control the strategy that can produces a higher profit module compared to that of the same level of risk.

• The Korean Journal of Applied Statistics
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• v.28 no.3
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• pp.393-406
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• 2015

We examine how to model mortality risk using the adaptation of the mean-reverting processes for the two factor model proposed by Cairns et al. (2006b). Mortality improvements have been recently observed in some countries such as United Kingdom; therefore, we assume long-run mortality converges towards a trend at some unknown time and the mean-reverting processes could therefore be an appropriate stochastic model. We estimate the parameters of the two-factor model incorporated with mean-reverting processes by a Metropolis-Hastings algorithm to fit United Kingdom mortality data from 1991 to 2015. We forecast the evolution of the mortality from 2014 to 2040 based on the estimation results in order to evaluate the issue price of a longevity bond of 25 years maturity. As an application, we propose a method to quantify the speed of mortality improvement by the average mean reverting times of the processes.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.2
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• pp.183-192
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• 2020

This study conducted an exploratory study to derive the success factors of entrepreneurial marketing for startups. Through Delphi analysis, we derived the items of proactive orientation, risk management, innovation, opportunity driven, resource leveraging, customer intensity, value creation, price setting, marketing communication, channel management, product development, sales and marketing information management. The importance and execution of success factors were measured through IPA analysis. The first quadrant(keep up the good work) has proactive orientation, risk management, resource leveraging, customer intensity. The second quadrant(concentrate here) has innovation. The third quadrant(low priority) has channel management, sales, marketing information management. The fourth quadrant(possible overkill) has prices setting, product development. These results showed that the success factors of startup's entrepreneurial marketing were proactive orientation, risk management, innovation, opportunity driven, resource leveraging, customer intensity and value creation.

• Journal of Information Technology Applications and Management
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• v.26 no.6
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• pp.29-46
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• 2019

This study analyzed the factors influencing the technology acceptance of the general public in the drones and ARs, one of the key technologies of the industry 4.0. The theoretical basis was the extended unified theory of acceptance and use of technology model(UTAUT2), which uses performance expectancy, effort expectancy, social influence, facilitating conditions, and hedonic motivation as factors common to both services. The price value factor was excluded considering that most ARs were free, and the perceived risk factors, including privacy, which were not in UTAUT2, were included because they are important factors for ICT technology acceptance. The hypothesis was tested by structure equation model. Social influence and hedonic motivation had a positive(+) effect on intention to use technology. On the other hand, in the case of effort expectancy, neither the AR nor the drone had a significant influence on intention to use technology. Furthermore, performance expectancy had a positive(+) effect on intention to use in AR, but no significant influence was found out in drones. On the contrary, in the case of the facilitating conditions, the influence of the drones was positive (+), but the relation of AR was not investigated. The perceived risk was tested for the negative (-) influence of use intention of AR, but no significant relationship was found out for the drones. Among the significant influencing factors, hedonic motivation was the most powerful factor in AR and drones. Theoretical and practical implications are presented based on these results.