Improving the Prediction Accuracy in Deep Learning-based Cryptocurrency Price Prediction

Abstract

Cryptocurrencies are popular today even though they do not have a physical form with their high profit rates and increasing usage day by day. However, the volatility of cryptocurrencies is higher than physical currencies. These volatilities change with the effect of social media rather than changes in exchange rates of physical currencies. For this reason, in this study, using Twitter data, one of the most widely used social media tools, real-time analysis on the values of four cryptocurrencies with the highest market value and the change in the estimated success compared to classical approaches were examined. The basic steps of this study: Obtaining Twitter data and financial data, performing sentiment analysis using Twitter data, making predictions on MM-LSTM architecture. The approach is aimed to be a predictive method open to online learning. Various filter steps were applied to remove the effect of bot users on Twitter that could prevent the prediction performance on the created data set, and the effect of the method on accuracy rate was tried to be reduced by eliminating the activity of bot accounts.

Keywords

• Forecasting
• Twitter sentiment score
• MM-LSTM
• Cryptocurrency
• Deep learning

References

1. S. Nakamoto, Bitcoin: A peer-to-peer electronic cash system, Decentralized Business Review, 2008.
2. H. Wang, D. Can, A. Kazemzadeh, F. Bar and S. Narayanan, A System for Real-time Twitter Sentiment Analysis of 2012 U.S. Presidential Election Cycle, Proceedings of the ACL 2012 System Demonstrations, pp. 115-120, 2012.
3. D. R. Pant, P. Neupane, A. Poudel, A. K. Pokhrel and B. K. Lama, Recurrent neural network based bitcoin price prediction by twitter sentiment analysis, 2018 IEEE 3rd International Conference on Computing, Communication and Security, Kathmandu, Nepal, (2018) 128-132.
4. J. Bollen, H. Mao and X. Zeng, Twitter mood predicts the stock market, Journal of computational science, (2011) 2.1: 1-8. X. Li and C. A. Wang, The technology and economic determinants of cryptocurrency exchange rates: The case of Bitcoin, Decision support systems, (2017) 95: 49-60.
5. P. Ciaian, M. Rajcaniova and D. Kancs, The economics of Bitcoin price formation, Applied Economics, (2016) 48.19: 1799-1815.
6. D. G. Baur, K. Hong and A. D. Lee, Bitcoin: Medium of Exchange or Speculative Assets?, Journal of International Financial Markets, Institutions and Money, (2018) 54: 177-189.
7. A. H. Dyhrberg, Bitcoin, gold and the dollar–A GARCH volatility analysis, Finance Research Letters, (2016) 16: 85-92.
8. N. Gandal, Price manipulation in the Bitcoin ecosystem, Journal of Monetary Economics, (2018) 95: 86-96.

9. M. Baker and J. Wurgler, Investor sentiment and the cross-section of stock returns, The journal of Finance, (2006) 61.4: 1645-1680.
10. Y. Sovbetov, Factors influencing cryptocurrency prices: Evidence from bitcoin, ethereum, dash, litcoin, and monero, Journal of Economics and Financial Analysis, (2018) 2.2: 1-27.
11. O. Poyser, Exploring the dynamics of Bitcoin’s price: a Bayesian structural time series approach, Eurasian Economic Review, (2019) 9.1: 29-60.
12. Z. H. Munim, M. H. Shakil and I. Alon, Next-day bitcoin price forecast, Journal of Risk and Financial Management, (2019) 12.2: 103.
13. M. Polasik, A. I. Piotrowska, T. P. Wisniewski, R. Kotkowski and G. Lightfoot, Price fluctuations and the use of bitcoin: An empirical inquiry, International Journal of Electronic Commerce, (2015) 20.1: 9-49.
14. T. Rao and S. Srivastava, Modeling movements in oil, gold, forex and market indices using search volume index and twitter sentiments, Proceedings of the 5th annual ACM Web science conference, Paris, France, (2013) 336-345.
15. V. Karalevicius, N. Degrande and J. De Weerdt, Using sentiment analysis to predict interday Bitcoin price movements, The Journal of Risk Finance, (2018) 19.1: 56-75.
16. C. WJ. Granger, Investigating causal relations by econometric models and cross-spectral methods, Econometrica: journal of the Econometric Society, (1969) 424-438.
17. L. Deng and D. Yu, Deep learning: methods and applications, Foundations and trends in signal processing, (2014) 7.3–4: 197-387.
18. D. Ciregan, U. Meier and J. Schmidhuber, Multi-column deep neural networks for image classification, 2012 IEEE conference on computer vision and pattern recognition, USA, (2012) 3642-3649.
19. K. Fukushima and S. Miyake, Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition, Competition and cooperation in neural nets, (1982) 267-285.
20. F. Balcı, Z. Oralhan, LSTM ile EEG Tabanlı Kimliklendirme Sistemi Tasarımı, Avrupa Bilim ve Teknoloji Dergisi, (2020) 135-141.
21. M. F. Stollenga, B. Wonmin, M. Liwicki and J. Schmidhuber, Parallel multi-dimensional LSTM, with application to fast biomedical volumetric image segmentation, Advances in neural information processing systems, (2015) 28: 2998-3006.
22. S. Song, H. Huang and T. Ruan, Abstractive text summarization using LSTM-CNN based deep learning, Multimedia Tools and Applications, (2019) 78.1: 857-875.
23. Y. Yin, X. Zheng, B. Hu, Y. Zhang and X. Cui, EEG emotion recognition using fusion model of graph convolutional neural networks and LSTM, Applied Soft Computing, (2021) 100: 106954.
24. C. Wu, C. Lu, Y. Ma and R. Lu, A new forecasting framework for bitcoin price with LSTM, 2018 IEEE International Conference on Data Mining Workshops (ICDMW), Singapore, (2018) 168-175.
25. A. Aggarwal, I. Gupta, N. Garg and A. Goel, Deep learning approach to determine the impact of socio economic factors on bitcoin price prediction, 2019 Twelfth International Conference on Contemporary Computing (IC3), Noida, India, (2019) 1-5.
26. W. Yiying and Z. Yeze, Cryptocurrency price analysis with artificial intelligence, 2019 5th International Conference on Information Management (ICIM), Cambridge, UK, (2019) 97-101.
27. P. Yamak, L. Yujian and P. K. Gadosey, A comparison between arima, lstm, and gru for time series forecasting, 2nd International Conference on Algorithms, Computing and Artificial Intelligence, Sanya, China, (2019) 49-55.
28. W. Zhengyang, L. Xingzhou, R. Jinjin and K. Jiaqing, Prediction of cryptocurrency price dynamics with multiple machine learning techniques, 4th International Conference on Machine Learning Technologies, Nanchang, China, (2019) 15-19.