과제정보
이 논문은 2017년 대한민국 교육부와 한국연구재단의 지원을 받아 수행된 연구임(NRF-2017S1A5A2A01027625).
참고문헌
- 권혁준, 김협, 최재원 (2018). 개인 의료정보 보호를 위한 블록체인 적용 방안: 프라이빗 블록 스킴을 중심으로. 지식경영연구, 19(4), 119-131. https://doi.org/10.15813/kmr.2018.19.4.007
- 김선웅 (2021). 암호화폐 트레이딩시스템의 수익성 분석. 한국디지털콘텐츠학회 논문지, 22(3), 555-562.
- 김효상 (2019). 암호화자산이 국경 간 자본흐름에 미치는 영향. 국제금융연구, 9(2), 67-90. https://doi.org/10.34251/IFADOI.9.2.201911.003
- 배성완, 유정석 (2018). 머신 러닝 방법과 시계열 분석 모형을 이용한 부동산 가격지수 예측. 주택연구, 26(1), 107-133.
- 정윤경, 하예영, 이혜인, 양희동 (2020). 공유경제 체제로서컨소시엄 블록체인을 활용한 와인투자 주식플랫폼 프레임워크. 지식경영연구, 21(3), 45-65. https://doi.org/10.15813/KMR.2020.21.3.003
- 조보근, 박경배, 하성호 (2020). 기계학습 알고리즘을 활용한 지역 별 아파트 실거래가격지수 예측모델 비교: LIME 해석력 검증. 정보시스템연구, 29(3), 119-144.
- 최재원 (2018). 건강정보에 대한 블록체인 기술 응용: 블록체인 기술은 글로벌 건강 정보 이슈에 대해 만병 통치약이 될 수 있는가? 지식경영연구, 19(4), 187-201.
- 허인 (2019). 비트코인 시장의 변동성과 전통 금융시장과의 관계. 시장경제연구, 48(2), 53-87.
- Abad, C., Thore, S. A., & Laffarga, J. (2004). Fundamental analysis of stocks by two-stage DEA. Managerial and Decision Economics, 25(5), 231-241. https://doi.org/10.1002/mde.1145
- Azari, A. (2019). Bitcoin price prediction: An ARIMA approach. arXiv preprint arXiv:1904.05315.
- Brandvold, M., Molnar, P., Vagstad, K., & Valstad, O. C. A. (2015). Price discovery on Bitcoin exchanges. Journal of International Financial Markets, Institutions and Money, 36, 18-35. https://doi.org/10.1016/j.intfin.2015.02.010
- Chang, P. C. (2012). A novel model by evolving partially connected neural network for stock price trend forecasting. Expert Systems with Applications, 39(1), 611-620. https://doi.org/10.1016/j.eswa.2011.07.051
- Cheah, E. T., & Fry, J. (2015). Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin. Economics Letters, 130, 32-36. https://doi.org/10.1016/j.econlet.2015.02.029
- 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
- Chen, Z., Li, C., & Sun, W. (2020). Bitcoin price prediction using machine learning: An approach to sample dimension engineering. Journal of Computational and Applied Mathematics, 365, 112395. https://doi.org/10.1016/j.cam.2019.112395
- Ciaian, P., & Rajcaniova, M. (2018). Virtual relationships: Short-and long-run evidence from BitCoin and altcoin markets. Journal of International Financial Markets, Institutions and Money, 52, 173-195. https://doi.org/10.1016/j.intfin.2017.11.001
- Dillon, M. (1983). Introduction to modern information retrieval: G. Salton and M. McGill. McGraw-Hill, New York.
- Fauzi, M. A., Paiman, N., & Othman, Z. (2020). Bitcoin and cryptocurrency: Challenges, opportunities and future works. The Journal of Asian Finance, Economics and Business(JAFEB), 7(8), 695-704. https://doi.org/10.13106/JAFEB.2020.VOL7.NO8.695
- Felizardo, L., Oliveira, R., Del-Moral-Hernandez, E., & Cozman, F. (2019, October). Comparative study of Bitcoin price prediction using WaveNets, Recurrent Neural Networks and other Machine Learning Methods. In 2019 6th International Conference on Behavioral, Economic and Socio-Cultural Computing (BESC), IEEE, 1-6.
- Georgoula, I., Pournarakis, D., Bilanakos, C., Sotiropoulos, D., & Giaglis, G. M. (2015). Using time-series and sentiment analysis to detect the determinants of bitcoin prices. Available at SSRN 2607167.
- Hagenau, M., Liebmann, M., & Neumann, D. (2013). Automated news reading: Stock price prediction based on financial news using context-capturing features. Decision Support Systems, 55(3), 685-697. https://doi.org/10.1016/j.dss.2013.02.006
- Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679-688. https://doi.org/10.1016/j.ijforecast.2006.03.001
- Joo, T. W., & Kim, S. B. (2015). Time series forecasting based on wavelet filtering. Expert Systems with Applications, 42(8), 3868-3874. https://doi.org/10.1016/j.eswa.2015.01.026
- Karalevicius, V., Degrande, N., & De Weerdt, J. (2018). Using sentiment analysis to predict interday Bitcoin price movements. The Journal of Risk Finance, 19(1), 56-75. https://doi.org/10.1108/JRF-06-2017-0092
- Kodama, O., Pichl, L., & Kaizoji, T. (2017, September). Regime change and trend prediction for Bitcoin time series data. In CBU International Conference Proceedings, 5, 384-388.
- Kraus, M., & Feuerriegel, S. (2017). Decision support from financial disclosures with deep neural networks and transfer learning. Decision Support Systems, 104, 38-48. https://doi.org/10.1016/j.dss.2017.10.001
- Lamothe-Fernandez, P., Alaminos, D., LamotheLopez, P., & Fernandez-Gamez, M. A. (2020). Deep learning methods for modeling Bitcoin price. Mathematics, 8(8), 1245. https://doi.org/10.3390/math8081245
- Li, Y., Zheng, Z., & Dai, H. N. (2020). Enhancing Bitcoin price fluctuation prediction using attentive LSTM and embedding network. Applied Sciences, 10(14), 4872. https://doi.org/10.3390/app10144872
- Lu, N., Lin, H., Lu, J., & Zhang, G. (2012). A customer churn prediction model in telecom industry using boosting. IEEE Transactions on Industrial Informatics, 10(2), 1659-1665. https://doi.org/10.1109/TII.2012.2224355
- Luther, W. J. (2016). Bitcoin and the future of digital payments. The Independent Review, 20(3), 397-404.
- Makridakis, S. (1994). Time series prediction: Forecasting the future and understanding the past. International Journal of Forecasting, 10(3), 463-466. https://doi.org/10.1016/0169-2070(94)90077-9
- Mittermayer, M. A. (2004, January). Forecasting intraday stock price trends with text mining techniques. In 37th Annual Hawaii International Conference on System Sciences, IEEE, 10.
- Pawar, K., Jalem, R. S., & Tiwari, V. (2019). Stock market price prediction using LSTM RNN. In Emerging trends in expert applications and security (pp. 493-503). Springer, Singapore.
- Selvin, S., Vinayakumar, R., Gopalakrishnan, E. A., Menon, V. K., & Soman, K. P. (2017, September). Stock price prediction using LSTM, RNN and CNN-sliding window model. In 2017 International Conference on Advances in Computing, Communications and Informatics(ICACCI), IEEE, 1643-1647.
- Tang, X., Yang, C., & Zhou, J. (2009, September). Stock price forecasting by combining news mining and time series analysis. In 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, IEEE, 1, 279-282.
- Telli, S., & Chen, H. (2020). Structural breaks and trend awareness-based interaction in crypto markets. Physica A: Statistical Mechanics and Its Applications, 558, 124913. https://doi.org/10.1016/j.physa.2020.124913
- Velankar, S., Valecha, S., & Maji, S. (2018, February). Bitcoin price prediction using machine learning. In 2018 20th International Conference on Advanced Communication Technology(ICACT), IEEE, 144-147.
- Wolla, S. A. (2018). Bitcoin: Money or financial investment? Page One Economics®.
- Yao, W., Xu, K., & Li, Q. (2019, June). Exploring the influence of news articles on Bitcoin price with machine learning. In 2019 IEEE Symposium on Computers and Communications(ISCC), IEEE, 1-6.