Abstract
Cryptocurrency has high volatility in market price since its inception. Existing works have explored different models to predict cryptocurrency prices. However, the accuracy is not satisfactory. In this paper, we conduct empirical study on the price forecasting. Firstly, we quantify the entropy and the conditional entropy of cryptocurrencies and stocks, respectively, and find that cryptocurrencies are more difficult to predict than stocks. Secondly, we evaluate various perspectives, including Twitter volume, Twitter sentiment and CNN-LSTM price prediction. Empirical results demonstrated the randomness in price validity, thus no single method is robust enough for cryptocurrency price prediction.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Garcia, D., Schweitzer, F.: Social signals and algorithmic trading of Bitcoin. Royal Soc. Open Sci. 2(9), 150288 (2015)
Kristoufek, L.: What are the main Drivers of the Bitcoin Price? Evidence from Wavelet Cohernce Analysis (2015)
Greff, K., Srivastava, R.K., KoutnÃk, J., Steunebrink, B.R., Schmidhuber, J.: LSTM: a search space odyssey. IEEE Trans. Neural Networks Learn. Syst. 28(10), 2222–2232 (2016)
Jiang, Z., Liang, J.: September. Cryptocurrency portfolio management with deep reinforcement learning. In: IEEE Intelligent Systems Conference (IntelliSys), pp. 905–913 (2017)
Songmuang, K.: The forecasting of cryptocurrency price by correlation and regression analysis. Kasem Bundit J. 19(May–June), 287–296 (2018)
Colianni, S., Rosales, S., Signorotti, M.: Algorithmic trading of cryptocurrency based on Twitter sentiment analysis. CS229 Project, 1–5
Sasank Pagolu, V., Reddy, K.N., Panda, G., Majhi, B.: Sentiment analysis of Twitter data for predicting stock market movements. In: SCOPES (2016)
Li, X., Li, Y., Liu, X.-Y., Wang, D.: Risk management via anomaly circumvent: mnemonic deep learning for midterm stock prediction. In: KDD Workshop on Anomaly Detection in Finance (2019)
Cryptodatadownload. http://www.cryptodatadownload.com/. Accessed 20 July 2019
Coin Market Cap. https://coinmarketcap.com/. Accessed 10 July 2019
Xiong, Z., Liu, X.-Y., Zhong, S., Yang, H., Walid, A.: Practical deep reinforcement learning approach for stock trading. In: NeurIPS Workshop on Challenges and Opportunities for AI in Financial Services: the Impact of Fairness, Explainability, Accuracy, and Privacy (2018)
Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley, Hoboken (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Yang, L., Liu, XY., Li, X., Li, Y. (2019). Price Prediction of Cryptocurrency: An Empirical Study. In: Qiu, M. (eds) Smart Blockchain. SmartBlock 2019. Lecture Notes in Computer Science(), vol 11911. Springer, Cham. https://doi.org/10.1007/978-3-030-34083-4_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-34083-4_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-34082-7
Online ISBN: 978-3-030-34083-4
eBook Packages: Computer ScienceComputer Science (R0)