Journal of Intelligence and Information Systems (지능정보연구)

Korea Intelligent Information System Society (한국지능정보시스템학회)
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Cryptocurrency Recommendation Model using the Similarity and Association Rule Mining

유사도와 연관규칙분석을 이용한 암호화폐 추천모형

  • Kim, Yechan (Business Data Analytics MBA, Dongguk University) ;
  • Kim, Jinyoung (Business Data Analytics MBA, Dongguk University) ;
  • Kim, Chaerin (Business Data Analytics MBA, Dongguk University) ;
  • Kim, Kyoung-jae (Dept. of MIS, Dongguk University Seoul)
  • 김예찬 (동국대학교 서울 경영전문대학원) ;
  • 김진영 (동국대학교 서울 경영전문대학원) ;
  • 김채린 (동국대학교 서울 경영전문대학원) ;
  • 김경재 (동국대학교 서울 경영대학 경영정보학과)
  • Received : 2022.11.30
  • Accepted : 2022.12.12
  • Published : 2022.12.31
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Abstract

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.

최근 비트코인을 필두로한 암호화폐의 폭발적인 성장이 금융 시장의 주요 이슈로 떠오르고 있다. 이에 전 세계적인 암호화폐 투자의 관심이 증가하고 있지만, 24시간 365일 운영되는 시장과 가격 변동성, 그리고 기하 급수적으로 증가하고 있는 암호화폐 종류는 암호화폐 투자자들에게 리스크로 제공되고 있어, 특히 암호화폐 포트폴리오를 구상하는데 있어 추천에 적합하지 않는 암호화폐들을 구분하여 투자자들의 리스크를 감소시킬 수 있는 연구의 필요성이 제기되고 있다. 이에 본 논문은 기존에 있었던 단순히 암호화폐 가격의 미래를 예측하여 수익률을 극대화 하거나, 수익률에 초점을 맞추어 암호화폐 포트폴리오를 구성하는 연구들과 달리, 투자자들의 성향을 반영하고, 투자에 적합한 암호화폐를 머신러닝 기법 중 하나인 Apriori 알고리즘을 활용하여 추천하되, 추천에 적합한 알트코인들을 비트코인의 유사도와 연관규칙을 중심으로 선별하여, 투자자들의 리스크를 감소시킬 수 있는 적합한 추천 방식과 해석을 제시한다.

Keywords

References

  1. 김은미, 홍준우. (2020). 뉴스의 감성정보와 딥러닝을 이용한 암호화폐 가격 예측모형. 한국IT서비스학회 학술대회 논문집, 2020(3), 122-126.
  2. 김진민, 권용현. (2019). 암호화폐의 가격결정요인에 관한 실증연구: 규제리스크, 매스미디어 노출 및 신규상장 효과에 관하여. 정보사회와 미디어, 20(2), 1-29.
  3. 원종관, 홍태호. (2021). 텍스트 마이닝과 딥러닝을 활용한 암호화폐 가격 예측 : 한국과 미국시장 비교. 지식경영연구, 22(2), 1-17 https://doi.org/10.15813/KMR.2021.22.2.001
  4. 이준식, 김건우, 박도형. (2018). 비트코인 가격변화에 관한 실증분석. 지능정보연구, 24(2), 195-220. https://doi.org/10.13088/JIIS.2018.24.2.195
  5. 임준호. (2011). 연관규칙을 이용한 의료데이터 마이닝. 국내석사학위논문 고려대학교 컴퓨터정보통신대학원, 서울.
  6. 장성일, 김정연. (2017). 비트코인의 자산성격에 관한 연구. 한국전자거래학회지, 22(4), 117-128. https://doi.org/10.7838/JSEBS.2017.22.4.117
  7. 지혜영, 조완현. (2008). 다양한 데이터마이닝 분석법을 이용한 고객 금융서비스의 분석. Journal of The Korean Data Analysis Society (JKDAS), 10(3), 1527-1540.
  8. 한국은행 보도자료(2018.07), 암호자산과 중앙은행.
  9. Balfagih, A. M., & Keselj, V. (2019). Evaluating Sentiment C1assifiers for Bitcoin Tweets in Price Prediction Task. IEEE International Conference on Big Data (Big Data), 5499-5506.
  10. Dyhrberg, A. H. (2016). Bitcoin, gold and the dollar-A GARCH volatility analysis. Finance Research Letters, 16, 85-92. https://doi.org/10.1016/j.frl.2015.10.008
  11. Demir, E., Simonyan, S., Garcia-Gomez, C. D., & Lau, C. K. M. (2021). The asymmetric effect of bitcoin on altcoins: evidence from the nonlinear autoregressive distributed lag (NARDL) model. Finance Research Letters, 40, 101754. https://doi.org/10.1016/j.frl.2020.101754
  12. Gunay, S. (2019). Impact of public information arrivals on cryptocurrency market: A case of Twitter posts on ripple. East Asian Economic Review, 23(2), 149-168. https://doi.org/10.11644/kiep.eaer.2019.23.2.359
  13. Hwang, I. S. (2004). Item Selection by Estimated Profit Ranking based on Association Rule. Asia pacific journal of information systems, 14(4), 87-97. https://doi.org/10.1111/j.1365-2575.2004.00164.x
  14. Ryu, J. P., & Shin, H. J. (2021). Big Data Analysis of Financial Product Transaction Trends Using Associated Analysis. Journal of the Korea Convergence Society, 12(12), 49-57. https://doi.org/10.15207/JKCS.2021.12.12.049
  15. Rathan, K., Sai, S. V., & Manikanta, T. S. (2019). Crypto-currency price prediction using decision tree and regression techniques. In2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), 190-194.
  16. Koker, T. E., & Koutmos, D. (2020). Cryptocurrency trading using machine learning. Journal of Risk and Financial Management, 13(8), 178. https://doi.org/10.3390/jrfm13080178
  17. Lamon, C., Nielsen, E., & Redondo, E. (2017). Cryptocurrency price prediction using news and social media sentiment. SMU Data Sci, Rev, 1(3), 1-22.
  18. Nakano, M., & Takahashi, A. (2020). A new investment method with AutoEncoder: Applications to crypto currencies. Expert Systems with Applications, 162, 113730. https://doi.org/10.1016/j.eswa.2020.113730
  19. McNally, S., Roche, J., & Caton, S. (2018). Predicting the price of bitcoin using machine learning. In2018 26th euromicro international conference on parallel, distributed and network-based processing (PDP), 339-343
  20. Sami, H. M., & Arifuzzaman, S. M. (2021). Comparing Pure Stock Portfolio with Stock and Crypto-currency mixed Portfolio through LSTM to Compare & Analyze Investment Opportunities for Portfolio Performance Measurement. Aust. J. Eng. Innov. Technol, 3(3), 45-56.
  21. Chen, W., Zhang, H., Mehlawat, M. K., & Jia, L. (2021). Mean-variance portfolio optimization using machine learning-based stock price prediction. Applied Soft Computing, 100, 106943. https://doi.org/10.1016/j.asoc.2020.106943
  22. Chen, W., Xu, H., Jia, L., & Gao, Y. (2021). Machine learning model for Bitcoin exchange rate prediction using economic and technology determinants. International Journal of Forecasting, 37(1), 28-43. https://doi.org/10.1016/j.ijforecast.2020.02.008
  23. Ma, Y., Han, R., & Wang, W. (2021). Portfolio optimization with return prediction using deep learning and machine learning. Expert Systems with Applications, 165, 113973. https://doi.org/10.1016/j.eswa.2020.113973
  24. Zhu, Y., Dickinson, D., & Li, J. (2017). Analysis on the influence factors of Bitcoin's price based on VEC model. Financial Innovation,3(1), 1-13. https://doi.org/10.1186/s40854-016-0051-8
  25. Coinmarketcap, Cryptocurrency Historical Data Snapshot, 08. 12., 2022, https://coinmarketcap.com/historical/
  26. Coinmarketcap, Cryptos, 08. 12., 2022, https://coinmarketcap.com/
  27. Coinmarketcap, Total Cryptocurrency Market Cap, 04. 15., 2022, https://coinmarketcap.com/charts/
  28. TradingView, MARKET CAP BTC DOMINANCE, 08. 12., 2022, https://www.tradingview.com/symbols/CRYPTOCAP-BTC.D/
  29. TradingView, MARKET CAP ETH DOMINANCE, 08. 12., 2022, https://www.tradingview.com/chart/?symbol=ETH.D
  30. TradingView, MARKET CAP SOL DOMINANCE, 08. 12., 2022, https://www.tradingview.com/chart/?symbol=CRYPTOCAP%3ASOL.D