참고문헌
- S. Nakamoto. (2008). Bitcoin: A peer-to-peer electronic cash system. http://www.lopp.net/pdf/bitcoin.pdf
- H. J. Kim. (2018). Policy trends and issues on Crypto currency of Japanese government. Journal of the Korea Institute of Information and Communication Engineering, 22(10), 1398-1404. DOI : 10.6109/jkiice.2018.22.10.1398
- M. Vijh, D. Chandola, V. A. Tikkiwal & A. Kumar. (2020). Stock closing price prediction using machine learning techniques. Procedia Computer Science, 167, 599-606. DOI : 10.1016/j.procs.2020.03.326
- A. Yadav & C. K. Aditi Sharan. (2020). Optimizing LSTM for time series prediction in Indian stock market. International Conference on Computational Intelligence and Data Science, 167, 2091-2100. DOI : 10.1016/j.procs.2020.03.257
- D. H. Shin, K. H. Choi & C. B. Kim. (2017). Deep learning model for prediction rate improvement of stock price using RNN and LSTM. The Journal of Korean Institute of Information Technology, 15(10), 9-16. DOI : 10.14801/jkiit.2017.15.10.9
- J. U, P. Lu, C. Kim, U. Ryu & K. Pak. (2020). A new LSTM based reversal point prediction method using upward/downward reversal point feature sets. Chaos, Solitons and Fractals, 132, 1-15. DOI : 10.1016/j.chaos.2019.109559
- W. Li & J. Liao. (2017). A comparative study on trend forecasting approach for stock price time series. IEEE International Conference on Anti-counterfeiting, Security, and Identification, 74-78. DOI : 10.1109/ICASID.2017.8285747
- A. Moghar & M. Hamiche. (2020). Stock market prediction using LSTM recurrent neural network. Procedia Computer Science, 170, 1168-1173. DOI : 10.1016/j.procs.2020.03.049
- N. A. Bakar & S. Rosbi. (2017). Autoregressive integrated moving average model for forecasting cryptocurrency exchange rate in high volatility environment: A new insight of bitcoin transaction. International Journal of Advanced Engineering Research and Science, 4(11), 130-137. DOI : 10.22161/ijaers.4.11.20
- E. Demir, G. Gozgor, C. K. M. Lau & S. A. Vigne. (2018). Does economic policy uncertainty predict the Bitcoin returns? An empirical investigation. Finance Research Letters, 26, 145-149. DOI : 10.1016/j.frl.2018.01.005
- X. Li & C. Wang. (2017). The technology and economic determinants of cryptocurrency exchange rates: The case of Bitcoin. Decision Support Systems, 95, 49-60. DOI : 10.1016/j.dss.2016.12.001
- D. Aggarwal, S. Chandrasekaran, & B. Annamalai. (2020). A complete empirical ensemble mode decomposition ans support vector machine-based approach to predict Bitcoin prices. Journal of Behavioral and Experimental Finance, 27, 1-12. DOI : 10.1016/j.jbef.2020.100335
- D. Mallqui & R. Fernandes. (2019). Predicting the direction, maximum, minimum and closing prices of daily Bitcoin exchange rate using machine learning techniques. Applied Soft Computing Journal, 75, 596-606. DOI : 10.1016/j.asoc.2018.11.038
- M. Liu, G. Li, J. Li, X. Zhu & Y. Yao. (2020). Forecasting the price of Bitcoin using deep learning. Finance Research Letters, In press. DOI : 10.1016/j.frl.2020.101755
- S. Xiaolei, L. Mingxi & S. Zeqian. (2020). A novel cryptocurrency price trend forecasting model based on LightGBM. Finance Research Letters, 32, 1-6. DOI : 10.1016/j.frl.2018.12.032
- M. Gang, B. Kim, M. Shin, U. Baek & M. Kim. (2020). LSTM-based prediction of Bitcoin price fluctuation using sentiment analysis. Proceedings of Symposium of the Korean Institute of Communications and Information Sciences, 561-562.
- D. Pant, P. Neupane, A. Poudel, A. Pokhrel & B. Lama. (2018). Recurrent neural network based Bitcoin price prediction by Twitter sentiment analysis. International Conference on Computing, Communication and Security. DOI : 10.1109/CCCS.2018.85886824
- Y. Ahn & D. Kim. (2020). Emotional trading in the cryptocurrency market. Finance Research Letters, in Press. DOI : 10.1016/j.frl.2020.101912
- I. Makarov & A. Schoar. (2020). Trading and arbitrage in cryptocurrency markets. Journal of Financial Economics, 135, 293-319. DOI : 10.1016/j.jfineco.2019.07.001
- K. Tsang & Z. Yang. (2020). Price dispersion in bitcoin exchanges. Economics Letters, 194, 1-5. DOI : 10.1016/j.econlet.2020.109379
- E. Demir, G. Gozgor, C. Lau & S. Vigne. (2018). Does economic policy uncertainty predict the Bitcoin returns? An empirical investigation. Finance Research Letters, 26, 145-149. DOI : 10.1016/j.frl.2018.01.005
- G. Cohen. (2020). Forecasting Bitcoin trends using algorithmic learning systems. Entropy, 22, 1-11. DOI : 10.3390/e22080838
- Z. Chen, C. Li & W. Sun. (2020). Bitcoin price prediction using machine learning: An approach to sample dimension engineering. Journal of Computational and Applied Mathematics, 365, 1-13. DOI : 10.1016/j.cam.2019.112395
- S. Lahmiri & S. Bekiros. (2020). Intelligent forecasting with machine learning trading systems in chaotic intraday Bitcoin market. Chaos, Solitons and Fractals, 133, 1-7. DOI : 10.1016/j.chaos.2020.109641
- S. McNally, J. Roche & S. Caton. (2018). Predicting the price of Bitcoin using machine learning. 26th Euromicro International Conference on Parallel, Distributed, and Network-based Processing, 339-343. DOI : 10.1109/PDP2018.00060
- H. M. Kim & H. Kim. (2019). A study on predicting cryptocurrency distribution prices using machine learning techniques. Journal of Distribution Science, 10(11), 93-101. DOI : 10.13106/ijidb.2019.vol10.no11.93
- A. Urquhart. (2018). What causes the attention of Bitcoin?. Economics Letters, 166, 40-44. DOI : 10.1016/j.econlet.2018.02.017
- L. Liu. (2019). Are Bitcoin returns predictable? Evidence from technical indicators. Physica A, 533, 1-6. DOI : 10.1016/j.physa.2019.121950
- M. Nakano, A. Takahashi & S. Takahashi. (2018). Bitcoin technical trading with artificial neural network. Physica A, 510, 587-609. DOI : 10.1016/j.physa.2018.07.017
- T. L. Chen, C. H. Cheng & J. W. Liu. (2019). A causal time-series model based on multilayer perceptron regression for forecasting Taiwan stock index. International Journal of Information Technology & Decision Making, 18(6), 1967-1987. DOI : 10.1142/S0219622019500421
- N. Naik & B. Mohan. (2020). Intraday stock prediction based on deep neural network. National Academy Science Letters, 43, 241-246. DOI : 10.1007/s40009-019-00859-1
- O. Sezer & A. Ozbayoglu. (2018). Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing, 70, 525-538. DOI : 10.1016/j.asoc.2018.04.024
- S. Hochreiter & J. Schmidhuber. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780. DOI : 10.1162/neco.1997.9.8.1735
- J. Ilonen, J. Kamarainen & J. Lampinen. (2003). Differential evolution training algorithm for feed-forward neural networks. Neural Processing Letters, 17, 93-105. https://doi.org/10.1023/A:1022995128597
- H. J. Song, H. S. Choi, S. W. Kim & S. H. Oh. (2019). A study on financial time series data volatility prediction method using AI's LSTM method. Journal of Knowledge Information Technology and Systems, 14(6), 665-673. DOI : 10.34163/jkits.2019.14.6.009
- C. Olah. (2015). Understanding LSTM networks. https://colah.github.io/posts/2015-08-UnderstandingLSTMs/
- A. Bariviera. (2017). The inefficiency of Bitcoin revisited: A dynamic approach. Economics Letters, 161, 1-4. DOI : 10.1016/j.econlet.2017.09.013
- A. Phillip, J. Chan & S. Peiris. (2018). A new look at Cryptocurrencies. Economics Letters, 163, 6-9. DOI : 10.1016/j.econlet.2017.11.020