• Title/Summary/Keyword: Cryptocurrency

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A Study on the Design of LoRaWAN-based Public Blockchain Cryptocurrency Payment System (LoRaWAN 기반 공개형 블록체인 암호화폐 결제 시스템 설계를 위한 연구)

  • Kim, Minyoung
    • The Journal of the Convergence on Culture Technology
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    • v.7 no.1
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    • pp.608-614
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    • 2021
  • Currently, blockchain-based public cryptocurrency (hereinafter referred to as cryptocurrency) cannot acquire status as a currency for transaction due to the economic policies of each country, but it is used as an alternative currency transaction method due to individual circumstances of some Internet users. With this trend, it is predicted that such cryptocurrency can be used in real life beyond the Internet in the near future. In this paper, a technical method for designing a cryptocurrency payment system based on LoRaWAN that can easily build a wireless Internet network infrastructure at low cost as a way to activate the use of cryptocurrency in real life is presented based on the LoRaWAN standard.

Identifying Cryptocurrency Regulation Effects on Bitcoin Price : An Empirical Case in South Korea

  • Shamba, Kudzai;Jeon, Seong-Min
    • 한국벤처창업학회:학술대회논문집
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    • 2018.04a
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    • pp.187-190
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    • 2018
  • The study examines the effects of the regulation on cryptocurrency market, investigating a case in South Korea. As South Korea has one of the largest market share of the cryptocurrency market for the time being, its regulation in South Korea affected the entire markets around the World. This research in progress will use the method of difference-in-differences to assess the effects of regulation to the market. The findings indicate that there is a significant reduction of the Bitcoin price and the price volatility was significantly reduced by about 58% after the regulation of the cryptocurrency market. More so the trading activity indicates a huge decline after regulation was implemented.

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A Proposal on Cryptocurrency Dualization for Blockchain-based Artwork Trading System (블록체인 기반 예술품 거래 플랫폼을 위한 암호화폐 이원화 제안)

  • Lee, Eun Mi
    • Journal of the Korea Convergence Society
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    • v.10 no.12
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    • pp.215-221
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    • 2019
  • The development of blockchain-based art trading platforms has not been revitalized despite the recent recovery of the cryptocurrency market. In this paper, we found that blockchain-based art trading platform is not revitalized due to the large volatility of cryptocurrency price. As a solution, we propose a trading system using dual types of cryptocurrency that one is Stablecoin and the other is legacy cryptocurrency. Through cryptocurrency dualization, the proposed system can satisfy both user's requirements of stability of art price and value growth of the blockchain system. In addition, the proposed system is expected to be able to balance the use of dual cryptocurrencies and market capitalization ratios according to market principles. Finally, the proposed cryptocurrency dualization is expected to be used in other applications that require both the stability of the value of transactions and the growth of the value of the blockchain system.

A Study on the Tooling of Money Laundering Using Cryptocurrency (가상화폐를 이용한 자금세탁 도구화에 관한 연구)

  • Song, Hye Jin
    • Journal of the Society of Disaster Information
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    • v.17 no.3
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    • pp.600-607
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    • 2021
  • Purpose: The purpose of this study is to examine the path of money laundering of criminal proceeds through cryptocurrency using criminal script analysis and to devise measures to prevent and prevent criminal justice agencies from doing so. Method: Based on the results of a prior study on the profit path of cryptocurrency through money laundering and criminal cases in Korea, the path of money laundering was analyzed using criminal script techniques. Result: Most of the cryptocurrencies that have been launched are converted into criminal proceeds, which are re-launched and cashed or have a vicious cycle of being used as criminal funds are used. According to the script, the route of money laundering is mainly converted to criminal proceeds from cryptocurrency exchanges using anonymity, which is repeated several times, making it very difficult to find the money using cryptocurrency in criminal justice institutions. Conclusion: As the method of money laundering using cryptocurrency is becoming more sophisticated, legal sanctions and preventive institutionalization should be prepared for the prohibition or confiscation of cryptocurrency transactions for money laundering after understanding the flow.

Analysis of Distributed Cryptocurrency Exchange Model and Issues (분산 암호화폐 거래소 모델 및 이슈 분석)

  • Lee, Tae-Gyu
    • The Journal of the Convergence on Culture Technology
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    • v.8 no.1
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    • pp.583-590
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    • 2022
  • With the release of the Bitcoin source in 2009, cryptocurrencies are continuously developing and expanding the market. Recently, new applicability is expanding centered on NFT coin and metaverse payment service. In particular, the Central Cryptocurrency Exchange actively supports relay transactions between cryptocurrencies or between traditional fiat currencies and cryptocurrencies. The cryptocurrency trading market based on such a central exchange encouraged speculative factors of cryptocurrencies, strongly arousing speculation and futility of cryptocurrencies. In addition, the central cryptocurrency exchange induces the centralization of users and virtual assets, thereby hindering the decentralization and security enhancement strategies of the block chain. Therefore, this study describes the current status and problems of centrally controlled centralized cryptocurrency exchanges in service, and presents a distributed cryptocurrency exchange modeling strategy and major issues as a decentralization model of the exchange. This research can strengthen the anonymity, decentralization, and autonomy of cryptocurrency based on blockchain.

Cryptocurrency Recommendation Model using the Similarity and Association Rule Mining (유사도와 연관규칙분석을 이용한 암호화폐 추천모형)

  • Kim, Yechan;Kim, Jinyoung;Kim, Chaerin;Kim, Kyoung-jae
    • Journal of Intelligence and Information Systems
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    • v.28 no.4
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    • pp.287-308
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    • 2022
  • The explosive growth of cryptocurrency, led by Bitcoin has emerged as a major issue in the financial market recently. As a result, interest in cryptocurrency investment is increasing, but the market opens 24 hours and 365 days a year, price volatility, and exponentially increasing number of cryptocurrencies are provided as risks to cryptocurrency investors. For that reasons, It is raising the need for research to reduct investors' risks by dividing cryptocurrency which is not suitable for recommendation. Unlike the previous studies of maximizing returns by simply predicting the future of cryptocurrency prices or constructing cryptocurrency portfolios by focusing on returns, this paper reflects the tendencies of investors and presents an appropriate recommendation method with interpretation that can reduct investors' risks by selecting suitable Altcoins which are recommended using Apriori algorithm, one of the machine learning techniques, but based on the similarity and association rules of Bitocoin.

Distributed Ledger Technology and Cryptocurrency Market Potential Index (분산원장기술과 암호화폐시장 잠재력지수)

  • Nguyen, Kevin;Oh, Jeong-Hun
    • Informatization Policy
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    • v.27 no.2
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    • pp.20-39
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    • 2020
  • This paper introduces the Cryptocurrency Market Potential Index (CMPI) in order to measure the potential of the blockchain-backed cryptocurrency. Adopting the Distributed Ledger Technology (DLT) system as a conceptual framework, the whole process from development to implementation and adoption of blockchain-backed cryptocurrency are examined. This paper selects 30 variables and employs factor analysis for multivariate analysis to produce the CMPI for a total of 213 countries. The results show that although cryptocurrency is decentralized, its development and usage might still be very centralized in Europe, North America, hotspots in the Asia-Pacific, Middle East, and CIS regions. This result also highlights how important development and implementation are before adoption so that consequent financial transactions can take place.

Development of Deep Learning Ensemble Modeling for Cryptocurrency Price Prediction : Deep 4-LSTM Ensemble Model (암호화폐 가격 예측을 위한 딥러닝 앙상블 모델링 : Deep 4-LSTM Ensemble Model)

  • Choi, Soo-bin;Shin, Dong-hoon;Yoon, Sang-Hyeak;Kim, Hee-Woong
    • Journal of Information Technology Services
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    • v.19 no.6
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    • pp.131-144
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    • 2020
  • As the blockchain technology attracts attention, interest in cryptocurrency that is received as a reward is also increasing. Currently, investments and transactions are continuing with the expectation and increasing value of cryptocurrency. Accordingly, prediction for cryptocurrency price has been attempted through artificial intelligence technology and social sentiment analysis. The purpose of this paper is to develop a deep learning ensemble model for predicting the price fluctuations and one-day lag price of cryptocurrency based on the design science research method. This paper intends to perform predictive modeling on Ethereum among cryptocurrencies to make predictions more efficiently and accurately than existing models. Therefore, it collects data for five years related to Ethereum price and performs pre-processing through customized functions. In the model development stage, four LSTM models, which are efficient for time series data processing, are utilized to build an ensemble model with the optimal combination of hyperparameters found in the experimental process. Then, based on the performance evaluation scale, the superiority of the model is evaluated through comparison with other deep learning models. The results of this paper have a practical contribution that can be used as a model that shows high performance and predictive rate for cryptocurrency price prediction and price fluctuations. Besides, it shows academic contribution in that it improves the quality of research by following scientific design research procedures that solve scientific problems and create and evaluate new and innovative products in the field of information systems.

Understanding the Association Between Cryptocurrency Price Predictive Performance and Input Features (암호화폐 종가 예측 성능과 입력 변수 간의 연관성 분석)

  • Park, Jaehyun;Seo, Yeong-Seok
    • KIPS Transactions on Software and Data Engineering
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    • v.11 no.1
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    • pp.19-28
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    • 2022
  • Recently, cryptocurrency has attracted much attention, and price prediction studies of cryptocurrency have been actively conducted. Especially, efforts to improve the prediction performance by applying the deep learning model are continuing. LSTM (Long Short-Term Memory) model, which shows high performance in time series data among deep learning models, is applied in various views. However, it shows low performance in cryptocurrency price data with high volatility. Although, to solve this problem, new input features were found and study was conducted using them, there is a lack of study on input features that drop predictive performance. Thus, in this paper, we collect the recent trends of six cryptocurrencies including Bitcoin and Ethereum and analyze effects of input features on the cryptocurrency price predictive performance through statistics and deep learning. The results of the experiment showed that cryptocurrency price predictive performance the best when open price, high price, low price, volume and price were combined except for rate of closing price fluctuation.

Forecasting Cryptocurrency Prices in COVID-19 Phase: Convergence Study on Naver Trends and Deep Learning (COVID-19 국면의 암호화폐 가격 예측: 네이버트렌드와 딥러닝의 융합 연구)

  • Kim, Sun-Woong
    • Journal of Convergence for Information Technology
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    • v.12 no.3
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    • pp.116-125
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    • 2022
  • The purpose of this study is to analyze whether investor anxiety caused by COVID-19 affects cryptocurrency prices in the COVID-19 pandemic, and to experiment with cryptocurrency price prediction based on a deep learning model. Investor anxiety is calculated by combining Naver's Corona search index and Corona confirmed information, analyzing Granger causality with cryptocurrency prices, and predicting cryptocurrency prices using deep learning models. The experimental results are as follows. First, CCI indicators showed significant Granger causality in the returns of Bitcoin, Ethereum, and Lightcoin. Second, LSTM with CCI as an input variable showed high predictive performance. Third, Bitcoin's price prediction performance was the highest in comparison between cryptocurrencies. This study is of academic significance in that it is the first attempt to analyze the relationship between Naver's Corona search information and cryptocurrency prices in the Corona phase. In future studies, extended studies into various deep learning models are needed to increase price prediction accuracy.