Predicting Ethereum prices with machine learning based on Blockchain information

Abstract With the growing interest in cryptocurrency and its fundamental algorithm, studies of cryptocurrency price predictions have been actively conducted in various academic disciplines. Since cryptocurrency is generated and consumed by Blockchain systems, Blockchain-specific information can be considered as the main component in forecasting the values of cryptocurrency. This perspective has been widely adopted in studies of Bitcoin price predictions. However, we find that Ethereum, a popular and leading cryptocurrency in the market, has Blockchain information that differs from that of Bitcoin. Hence, this study investigates the relationship between inherent Ethereum Blockchain information and Ethereum prices. Furthermore, we investigate how Blockchain information concerning other publicly available coins on the market is associated with Ethereum prices. Our key findings reveal that macro-economy factors, Ethereum-specific Blockchain information, and the Blockchain information of other cryptocurrency play important roles in the prediction of Ethereum prices.

[1]  Hyeon-Eui Kim,et al.  Blockchain distributed ledger technologies for biomedical and health care applications , 2017, J. Am. Medical Informatics Assoc..

[2]  Ricardo A. S. Fernandes,et al.  Predicting the direction, maximum, minimum and closing prices of daily Bitcoin exchange rate using machine learning techniques , 2019, Appl. Soft Comput..

[3]  John Nelson,et al.  Cryptocurrency Price Prediction Using Tweet Volumes and Sentiment Analysis , 2018 .

[4]  Enrico Del Re,et al.  Energy Efficiency Perspectives of PMR Networks , 2017, Inf..

[5]  Ye Guo,et al.  Blockchain application and outlook in the banking industry , 2016, Financial Innovation.

[6]  Wei Jiang,et al.  Healthcare Data Gateways: Found Healthcare Intelligence on Blockchain with Novel Privacy Risk Control , 2016, Journal of Medical Systems.

[7]  Sooyong Park,et al.  Where Is Current Research on Blockchain Technology?—A Systematic Review , 2016, PloS one.

[8]  Zhongyi Hu,et al.  Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting , 2014, Knowl. Based Syst..

[9]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[10]  Ping Wang,et al.  Pricing currency options with support vector regression and stochastic volatility model with jumps , 2011, Expert Syst. Appl..

[11]  P. Ciaian,et al.  The economics of BitCoin price formation , 2014, 1405.4498.

[12]  Kevin P. Murphy,et al.  Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.

[13]  Xiaohui Wang,et al.  Time Series Prediction Methods for Depth-Averaged Current Velocities of Underwater Gliders , 2017, IEEE Access.

[14]  Ertuğrul Çam,et al.  Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines , 2015 .

[15]  Jaewook Lee,et al.  An Empirical Study on Modeling and Prediction of Bitcoin Prices With Bayesian Neural Networks Based on Blockchain Information , 2018, IEEE Access.

[16]  Ladislav Kristoufek,et al.  BitCoin meets Google Trends and Wikipedia: Quantifying the relationship between phenomena of the Internet era , 2013, Scientific Reports.

[17]  Alfonso Gómez-Espinosa,et al.  Price Movement Prediction of Cryptocurrencies Using Sentiment Analysis and Machine Learning , 2019, Entropy.

[18]  Obryan Poyser.  Exploring the dynamics of Bitcoin’s price: a Bayesian structural time series approach , 2018, Eurasian Economic Review.

[19]  P. Schwille,et al.  Discovery of 505-million-year old chitin in the basal demosponge Vauxia gracilenta , 2013, Scientific Reports.

[20]  Ömer Kaan Baykan,et al.  Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange , 2011, Expert Syst. Appl..

[21]  Jayadeep Pati,et al.  A Comparison Among ARIMA, BP-NN, and MOGA-NN for Software Clone Evolution Prediction , 2017, IEEE Access.

[22]  Zina Ben Miled,et al.  A Distributed Ledger for Supply Chain Physical Distribution Visibility , 2017, Inf..

[23]  Young Bin Kim,et al.  Predicting Fluctuations in Cryptocurrency Transactions Based on User Comments and Replies , 2016, PloS one.

[24]  Aziz Mohaisen,et al.  Toward Characterizing Blockchain-Based Cryptocurrencies for Highly Accurate Predictions , 2020, IEEE Systems Journal.

[25]  Ricardo A. S. Fernandes,et al.  Maximum and minimum stock price forecasting of Brazilian power distribution companies based on artificial neural networks , 2015, Appl. Soft Comput..

[26]  Zhetao Li,et al.  Consortium Blockchain for Secure Energy Trading in Industrial Internet of Things , 2018, IEEE Transactions on Industrial Informatics.