텍스트 마이닝과 딥러닝을 활용한 암호화폐 가격 예측 : 한국과 미국시장 비교
The Prediction of Cryptocurrency on Using Text Mining and Deep Learning Techniques : Comparison of Korean and USA Market
0
조회
0
다운로드
8
인용
0
참고문헌
키워드
초록
본 연구에서는 한국과 미국의 대표적인 거래소인 빗썸과 코인베이스의 비트코인 가격을 ARIMA와 순환 신경망(Recurrent Neural Network)을 이용해 예측하고, 이후 각 국가의 뉴스 기사를 이용해 분리 학습에 기반한 separated RNN 모형을 제안한다. separated RNN 모형은 학습 데이터를 가격의 추세 변화 점을 기준으로 분리해 학습시킨 후, 추세 변화점 별 뉴스 데이터를 활용해 용어 기반 사전을 구축한다. 이후 용어 기반 사전과 평가 데이터 기간의 뉴스 데이터를 이용해 예측할 데이터의 가격 추세 변화 점을 찾아낸 후, 매칭되는 모형을 적용해 예측 결과를 산출한다. 2017년 5월 22일부터 2020년 9월 16일까지의 가격 데이터를 사용해 분석한 결과, 제안된 separated RNN을 이용해 예측한 결과가 한국과미국의 비트코인 가격 예측 모두에서 순환 신경망(RNN)을 이용해 예측한 결과보다 높은 예측 성과를 보였다. 본 연구는시계열 예측 기법의 한계를 뉴스 데이터를 이용한 추세 변화 점 탐색을 통해 극복할 수 있고, 성과 향상을 위한 추후다양한 시계열 예측 기법 및 추세 변화 점 탐색을 위한 다양한 텍스트 마이닝 기법을 적용해볼 필요가 있음을 시사한다.
Abstract
In this study, we predicted the bitcoin prices of Bithum and Coinbase, a leading exchange in Korea and USA, using ARIMA and Recurrent Neural Networks(RNNs). And we used news articles from each country to suggest a separated RNN model. The suggested model identifies the datasets based on the changing trend of prices in the training data, and then applies time series prediction technique(RNNs) to create multiple models. Then we used daily news data to create a term-based dictionary for each trend change point. We explored trend change points in the test data using the daily news keyword data of testset and term-based dictionary, and apply a matching model to produce prediction results. With this approach we obtained higher accuracy than the model which predicted price by applying just time series prediction technique. This study presents that the limitations of the time series prediction techniques could be overcome by exploring trend change points using news data and various time series prediction techniques with text mining techniques could be applied to improve the performance of the model in the further research.
참고문헌 (39)
- 권혁준, "개인 의료정보 보호를 위한 블록체인 적용 방안: 프라이빗 블록 스킴을 중심으로", 지식경영연구, 19(4), pp.119~131 (2018), 한국지식경영학회
- 김선웅, "암호화폐 트레이딩시스템의 수익성 분석", 디지털콘텐츠학회논문지, 22(3), pp.555~562 (2021), 한국디지털콘텐츠학회[DOI]
- 김효상, "암호화자산이 국경 간 자본흐름에 미치는 영향", 국제금융연구, 9(2), pp.67~90 (2019), 한국국제금융학회
- 배성완, "머신 러닝 방법과 시계열 분석 모형을 이용한 부동산 가격지수 예측", 주택연구, 26(1), pp.107~133 (2018), 한국주택학회[DOI]
- 정윤경, "공유경제 체제로서 컨소시엄 블록체인을 활용한 와인투자 주식플랫폼 프레임워크", 지식경영연구, 21(3), pp.45~65 (2020), 한국지식경영학회[DOI]
- 조보근, "기계학습 알고리즘을 활용한 지역 별 아파트 실거래가격지수 예측모델 비교: LIME 해석력 검증", 정보시스템연구, 29(3), pp.119~144 (2020), 한국정보시스템학회
- 자야 수흐바트, "Blockchain technology usage on health equity: Is Blockchain technology a panacea for global health equity issues?", 지식경영연구, 19(4), pp.187~201 (2018), 한국지식경영학회[DOI]
- 허인, "비트코인 시장의 변동성과 전통 금융시장과의 관계", 시장경제연구, 48(2), pp.53~87 (2019), 지암남덕우경제연구원[DOI]
- Abad, C., "Fundamental analysis of stocks by two-stage DEA", Managerial and Decision Economics, 25(5), pp.231~241 (2004)
- Azari, A., "Bitcoin price prediction: An ARIMA approach", arXiv preprint arXiv:1904.05315, ~ (2019)
- Brandvold, M., "Price discovery on Bitcoin exchanges", Journal of International Financial Markets, Institutions and Money, 36, pp.18~35 (2015)
- Chang, P. C., "A novel model by evolving partially connected neural network for stock price trend forecasting", Expert Systems with Applications, 39(1), pp.611~620 (2012)
- Cheah, E. T., "Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin", Economics Letters, 130, pp.32~36 (2015)
- Chen, W., "Machine learning model for Bitcoin exchange rate prediction using economic and technology determinants", International Journal of Forecasting, 37(1), pp.28~43 (2021)
- Chen, Z., "Bitcoin price prediction using machine learning : An approach to sample dimension engineering", Journal of Computational and Applied Mathematics, 365, pp.112395~ (2020)
- Ciaian, P., "Virtual relationships: Short-and long-run evidence from BitCoin and altcoin markets", Journal of International Financial Markets, Institutions and Money, 52, pp.173~195 (2018)
- Dillon, M., "Introduction to modern information retrieval: G. Salton and M. McGill" (1983), McGraw-Hill
- Muhammad Ashraf FAUZI, "Bitcoin and Cryptocurrency: Challenges, Opportunities and Future Works", The Journal of Asian Finance, Economics and Business, 7(8), pp.695~704 (2020), 한국유통과학회
- Felizardo, L., "Comparative study of Bitcoin price prediction using WaveNets, Recurrent Neural Networks and other Machine Learning Methods", 2019 6th International Conference on Behavioral, Economic and Socio-Cultural Computing (BESC), IEEE, pp.1~6 (2019)
- Georgoula, I., "Using time-series and sentiment analysis to detect the determinants of bitcoin prices", SSRN 2607167, ~ (2015)
- Hagenau, M., "Automated news reading : Stock price prediction based on financial news using context-capturing features", Decision Support Systems, 55(3), pp.685~697 (2013)
- Hyndman, R. J., "Another look at measures of forecast accuracy", International Journal of Forecasting, 22(4), pp.679~688 (2006)
- Joo, T. W., "Time series forecasting based on wavelet filtering", Expert Systems with Applications, 42(8), pp.3868~3874 (2015)
- Karalevicius, V., "Using sentiment analysis to predict interday Bitcoin price movements", The Journal of Risk Finance, 19(1), pp.56~75 (2018)
- Kodama, O., "Regime change and trend prediction for Bitcoin time series data", CBU International Conference Proceedings, 5, pp.384~388 (2017)
- Kraus, M., "Decision support from financial disclosures with deep neural networks and transfer learning", Decision Support Systems, 104, pp.38~48 (2017)
- Lamothe-Fernández, P., "Deep learning methods for modeling Bitcoin price", Mathematics, 8(8), pp.1245~ (2020)
- Li, Y., "Enhancing Bitcoin price fluctuation prediction using attentive LSTM and embedding network", Applied Sciences, 10(14), pp.4872~ (2020)
- Lu, N., "A customer churn prediction model in telecom industry using boosting", IEEE Transactions on Industrial Informatics, 10(2), pp.1659~1665 (2012)
- Luther, W. J., "Bitcoin and the future of digital payments", The Independent Review, 20(3), pp.397~404 (2016)
- Makridakis, S., "Time series prediction : Forecasting the future and understanding the past", International Journal of Forecasting, 10(3), pp.463~466 (1994)
- Mittermayer, M. A., "Forecasting intraday stock price trends with text mining techniques", 37th Annual Hawaii International Conference on System Sciences, IEEE, pp.10~ (2004)
- Pawar, K., "Emerging trends in expert applications and security", pp.493~503 (2019), Springer
- Selvin, S., "Stock price prediction using LSTM, RNN and CNN-sliding window model", 2017 International Conference on Advances in Computing, Communications and Informatics(ICACCI), IEEE, pp.1643~1647 (2017)
- Tang, X., "Stock price forecasting by combining news mining and time series analysis", 2009 IEEE, ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, IEEE, pp.2009, 1[DOI]
- Telli, Ş., "Structural breaks and trend awareness-based interaction in crypto markets", Physica A : Statistical Mechanics and Its Applications, 558, pp.124913~ (2020)
- Velankar, S., "Bitcoin price prediction using machine learning", 201820th International Conference on Advanced Communication Technology(ICACT), IEEE, pp.144~147 (2018)
- Wolla, S. A., "Bitcoin: Money or financial investment?", Page One Economics®., ~ (2018)
- Yao, W., "Exploring the influence of news articles on Bitcoin price with machine learning", 2019 IEEE Symposium on Computers and Communications(ISCC), IEEE, pp.1~6 (2019)