• Journal of information and communication convergence engineering
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• v.22 no.1
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• pp.64-69
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• 2024

Blockchain is a decentralized network in which data blocks are linked. Through a decentralized peer-to-peer network, users can create shared databases, resulting in a trustworthy and aggregated database known as a blockchain that enhances reliability and security. The distributed nature of the blockchain enables data to be stored on multiple nodes, eliminating the need for a central server or platform. This disintermediation significantly reduces the transaction and administrative costs. The blockchain is particularly valuable in applications where reliability and stability are critical because it establishes an open database that ensures data integrity, making it virtually impossible to tamper with or falsify data. This study explores the diverse applications of the blockchain technology in virtual assets, such as cryptocurrency, decentralized finance, central bank digital currency, nonfungible tokens, and metaverses. In addition, it analyzes the potential prospects and developments driven by these innovative technologies.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.195-220
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• 2018

In this study, we conducted an empirical analysis of the factors that affect the change of Bitcoin Closing Price. Previous studies have focused on the security of the block chain system, the economic ripple effects caused by the cryptocurrency, legal implications and the acceptance to consumer about cryptocurrency. In various area, cryptocurrency was studied and many researcher and people including government, regardless of country, try to utilize cryptocurrency and applicate to its technology. Despite of rapid and dramatic change of cryptocurrencies' price and growth of its effects, empirical study of the factors affecting the price change of cryptocurrency was lack. There were only a few limited studies, business reports and short working paper. Therefore, it is necessary to determine what factors effect on the change of closing Bitcoin price. For analysis, hypotheses were constructed from three dimensions of consumer, industry, and macroeconomics for analysis, and time series data were collected for variables of each dimension. Consumer variables consist of search traffic of Bitcoin, search traffic of bitcoin ban, search traffic of ransomware and search traffic of war. Industry variables were composed GPU vendors' stock price and memory vendors' stock price. Macro-economy variables were contemplated such as U.S. dollar index futures, FOMC policy interest rates, WTI crude oil price. Using above variables, we did times series regression analysis to find relationship between those variables and change of Bitcoin Closing Price. Before the regression analysis to confirm the relationship between change of Bitcoin Closing Price and the other variables, we performed the Unit-root test to verifying the stationary of time series data to avoid spurious regression. Then, using a stationary data, we did the regression analysis. As a result of the analysis, we found that the change of Bitcoin Closing Price has negative effects with search traffic of 'Bitcoin Ban' and US dollar index futures, while change of GPU vendors' stock price and change of WTI crude oil price showed positive effects. In case of 'Bitcoin Ban', it is directly determining the maintenance or abolition of Bitcoin trade, that's why consumer reacted sensitively and effected on change of Bitcoin Closing Price. GPU is raw material of Bitcoin mining. Generally, increasing of companies' stock price means the growth of the sales of those companies' products and services. GPU's demands increases are indirectly reflected to the GPU vendors' stock price. Making an interpretation, a rise in prices of GPU has put a crimp on the mining of Bitcoin. Consequently, GPU vendors' stock price effects on change of Bitcoin Closing Price. And we confirmed U.S. dollar index futures moved in the opposite direction with change of Bitcoin Closing Price. It moved like Gold. Gold was considered as a safe asset to consumers and it means consumer think that Bitcoin is a safe asset. On the other hand, WTI oil price went Bitcoin Closing Price's way. It implies that Bitcoin are regarded to investment asset like raw materials market's product. The variables that were not significant in the analysis were search traffic of bitcoin, search traffic of ransomware, search traffic of war, memory vendor's stock price, FOMC policy interest rates. In search traffic of bitcoin, we judged that interest in Bitcoin did not lead to purchase of Bitcoin. It means search traffic of Bitcoin didn't reflect all of Bitcoin's demand. So, it implies there are some factors that regulate and mediate the Bitcoin purchase. In search traffic of ransomware, it is hard to say concern of ransomware determined the whole Bitcoin demand. Because only a few people damaged by ransomware and the percentage of hackers requiring Bitcoins was low. Also, its information security problem is events not continuous issues. Search traffic of war was not significant. Like stock market, generally it has negative in relation to war, but exceptional case like Gulf war, it moves stakeholders' profits and environment. We think that this is the same case. In memory vendor stock price, this is because memory vendors' flagship products were not VRAM which is essential for Bitcoin supply. In FOMC policy interest rates, when the interest rate is low, the surplus capital is invested in securities such as stocks. But Bitcoin' price fluctuation was large so it is not recognized as an attractive commodity to the consumers. In addition, unlike the stock market, Bitcoin doesn't have any safety policy such as Circuit breakers and Sidecar. Through this study, we verified what factors effect on change of Bitcoin Closing Price, and interpreted why such change happened. In addition, establishing the characteristics of Bitcoin as a safe asset and investment asset, we provide a guide how consumer, financial institution and government organization approach to the cryptocurrency. Moreover, corroborating the factors affecting change of Bitcoin Closing Price, researcher will get some clue and qualification which factors have to be considered in hereafter cryptocurrency study.

• Food Science and Industry
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• v.54 no.3
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• pp.132-144
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• 2021

Along with the rise in the value of cryptocurrency, the interest of the blockchain is very high. However, most people do not understand cryptocurrency and blockchain very well. In addition, due to this lack of understanding of the technology, ideas about how blockchain technology can be applied in the food industry may not come up. Therefore, this content describes the advantages of blockchain technology in terms of security, starting with the understanding and operation method of cryptocurrency and blockchain technology, and briefly describes the development of a blockchain system. And simple examples of how this blockchain technology can be applied to other industries are summarized, and examples used in the food industry are summarized. Finally, it was insisted that using such a blockchain technology can provide safe food to consumers.

• KNOM Review
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• v.21 no.2
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• pp.18-25
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• 2018

Since the development of block-chain technology by Satoshi Nakamoto and Bitcoin pioneered a new cryptocurrency market, a number of scale of cryptocurrency have emerged. There are crimes taking place using the anonymity and vulnerabilities of block-chain technology, and many studies are underway to improve vulnerability and prevent crime. However, they are not enough to detect users who commit crimes. Therefore, it is very important to detect abnormal behavior such as money laundering and stealing cryptocurrency from the network. In this paper, the characteristics of the transactions and user graphs in the Bitcoin network are collected and statistical information is extracted from them and presented as plots on the log scale. Finally, we analyze visualized plots according to the Densification Power Law and Power Law Degree, as a result, present features appropriate for detection of anomalies involving abnormal transactions and abnormal users in the Bitcoin network.

• The Journal of the Convergence on Culture Technology
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• v.5 no.3
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• pp.55-60
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• 2019

It is true that Blockchain has been known as a core technology for cryptocurrency like bitcoin (BTC). It is caused by its rapid value rises. Now, one BTC is trading around 10,000 US dollars while it bought just less than one dollar at its first trading in May, 2010. Blockchain makes on-line transactions possible by the safe cryptocurrency swiftly based on P2P network and distributed public ledger while its on-line traffic is rapidly increasing. However, this technology has bigger potential in the fourth industrial revolution era and its application areas will be varied. The evolving intelligent information society needs to make new added value through utilizing, sharing and processing of useful digital information. Obstacles such as hacking and fraud often exist when transactions of digital properties, right transfers, etc. are done through digital network specialized with anonymity. It is expected that blockchain will be a definite solution in this regard. This paper addresses useful development directions and countermeasures for blokchain in the digital economy by analysis of its current status and issues.

• Journal of the Korea Convergence Society
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• v.12 no.4
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• pp.249-258
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• 2021

Bitcoin prices have been soaring recently as investors flock to cryptocurrency exchanges. The purpose of this study is to predict the Bitcoin price using a deep learning model and analyze whether Bitcoin is profitable through investment strategy. LSTM is utilized as Bitcoin prediction model with nonlinearity and long-term memory and the profitability of MA cross-over strategy with predicted prices as input variables is analyzed. Investment performance of Bitcoin strategy using LSTM forecast prices from 2013 to 2021 showed return improvement of 5.5% and 46% more than market price MA cross-over strategy and benchmark Buy & Hold strategy, respectively. The results of this study, which expanded to recent data, supported the inefficiency of the cryptocurrency market, as did previous studies, and showed the feasibility of using the deep learning model for Bitcoin investors. In future research, it is necessary to develop optimal prediction models and improve the profitability of Bitcoin investment strategies through performance comparison of various deep learning models.

• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.59-67
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• 2021

Blockchain technology began in 2008 when an unidentified person named Satoshi Nakamoto proposed a cryptocurrency called Bitcoin. Satoshi Nakamoto had distrust of the existing financial system and wanted to implement a financial system that is robust against hacking or mannipulation without a middleman such as a bank through blockchain technology. Satoshi proposed a blockchain as a technology to prevent the creation of the bitcoin and forging of transactions, and through this, the functions of issuance, transaction, and verification of currency were implemented. Since then, Ethereum, a cryptocurrency that can implement the smart contract on the blockchain, has been developed, allowing financial products that require complex contracts such as deposits, loans, insurance, and derivatives to be brought into the area of cryptocurrency. In addition, it is expanding the possibility of substituting products provided by financial institutions through combination with real assets. These applications are defined as Decentralized Finance (DeFi). This paper was prepared to understand the overall technical understanding of DeFi and to introduce the services currently in operation. First, the technologies and ecosystems that implement the overall DeFi are explained, and then the representative DeFi services are categorized by feature and described.

• The Korean Journal of Applied Statistics
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• v.35 no.2
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• pp.189-201
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• 2022

In this study, we propose factor augmentation to improve forecasting power of cryptocurrency return. We consider financial and economic variables as well as psychological aspect for possible factors. To be more specific, financial and economic factors are obtained by applying principal factor analysis. Psychological factor is summarized by news sentiment analysis. We also visualize such factors through impulse response analysis. In the modeling perspective, we consider ARIMAX as the classical model, and random forest and deep learning to accommodate nonlinear features. As a result, we show that factor augmentation reduces prediction error and the GRU performed the best amongst all models considered.

• Journal of Digital Convergence
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• v.19 no.11
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• pp.359-364
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• 2021

Recently, research on predictive systems using deep learning and machine learning of artificial intelligence is being actively conducted. Due to the development of artificial intelligence, the role of the investment manager is being replaced by artificial intelligence, and due to the higher rate of return than the investment manager, algorithmic trading using artificial intelligence is becoming more common. Algorithmic trading excludes human emotions and trades mechanically according to conditions, so it comes out higher than human trading yields when approached in the long term. The deep learning technique of artificial intelligence learns past time series data and predicts the future, so it learns like a human and can respond to changing strategies. In particular, the LSTM technique is used to predict the future by increasing the weight of recent data by remembering or forgetting part of past data. fbprophet, an artificial intelligence algorithm recently developed by Facebook, boasts high prediction accuracy and is used to predict stock prices and cryptocurrency prices. Therefore, this study intends to establish a sound investment culture by providing a new algorithm for automatic cryptocurrency trading by analyzing the actual value and difference using fbprophet and presenting conditions for accurate prediction.

• Journal of Information Technology Applications and Management
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• v.26 no.6
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• pp.47-63
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• 2019

The volatility of major crypto currencies was examined and they are diagnosed whether they have a systematic risk or not, by estimating market beta representing systematic risk using GARCH( Generalized Auto Regressive Conditional Heteroskedastieity) model. First, the empirical results showed that their prices are very volatile over time because of the existence of ARCH and GARCH effects. Second, in terms of efficiency, asymmetric GJR model was estimated to be the most appropriate model because the standard error of a market beta was less than that of the OLS model and GARCH model. Third, the estimated market beta of Bitcoin using GJR model was less than 1 at 0.8791, showing that there is no systematic risk. However, unlike OLS model, the market beta of Ethereum and Ripple was estimated at 1.0581 and 1.1222, showing that there is systematic risk. This result shows that bitcoin is less dangerous than Ripple and Ethereum, and ripple is the most dangerous of all three crypto currencies. Finally, the major cryptocurrency found that the negative impact caused greater variability than the positive impact, causing bad news to fluctuate more than good news, and therefore good news and bad news had a different effect on the variability.