Predicting the closing price of cryptocurrency Ethereum
Keywords:
Cryptocurrency;, Ethereum;, Machine learning models;, Natural language processing;, Recurrent neural network.
Abstract
Given that cryptocurrencies are now involved in nearly every financial transaction due to their widespread acceptance as an alternative method of payment and currency exchange, researchers and economists have increased opportunities to analyze cryptocurrency prices. Over time, predicting the daily closing price of Ethereum has been challenging for investors, traders, and investment banks because of its significant price volatility. The daily closing price of cryptocurrency is crucial for trading or investing in Ethereum. This report aims to conduct a comparative analysis of the predictive performance of deep machine learning algorithms within a stacking ensemble modeling framework, utilizing daily historical price data of Ethereum from Coindesk, tweets from Twitter spanning from August 1, 2022, to August 8, 2022, and five additional covariates (closing price lag1, closing price lag2, noltrend, daytype, and month) derived from Ethereum's closing price. Seven models are employed to forecast the daily closing price of Ethereum: recurrent neural network, ensemble stacked recurrent neural network, gradient boosting machine, generalized linear model, distributed random forest, deep neural networks, and a stacked ensemble of gradient boosting machine, generalized linear model, distributed random forest, and deep neural networks. The primary evaluation metric is the mean absolute error (MAE). Based on MAE, the RNN forecasts outperform the other models in this study, achieving an MAE of 0.0309.References
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[4] Times, T. E. (2021), ‘Cryptocurrency ethereumis flourishing but risks linger’. https://economictimes.indiatimes.com/markets/forex/
cryptocurrencyethereum-is-flourishing-but-risks-linger/articleshow/82475252.cms
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[6] Anderson, J. R. (1990),Machine learning: An artificial intelligence approach, Vol. 3,Morgan Kaufmann.
[7] Liddy, E. D. (2001), ‘Natural language processing’.
[8] Fleming, J., Ostdiek, B. and Whaley, R. E. (1995), ‘Predicting stock market volatility: A new measure’, Journal of FuturesMarkets 15(3), 265–302.
[9] Jain, P. and Jiang, C. (2014), ‘Predicting future price volatility: empirical evidence from an emerging limit order market’, Pacific-Basin Finance Journal 27, 72–93.
[10] Tang, Y., Xiao, X.,Wahab, M. andMa, F. (2021), ‘The role of oil futures intraday information on predicting us stock market volatility’, Journal of Management Science and Engineering 6(1), 64–74.
[11] Lahmiri, S. (2017), ‘Modeling and predicting historical volatility in exchange rate markets’, Physica A: StatisticalMechanics and its Applications 471, 387–395.
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[14] Derbentsev, V., Babenko, V., Khrustalev, K.I.R.I.L.L., Obruch, H. and Khrustalova, S.O.F.I.I.A. (2021). Comparative performance of machine learning ensemble algorithms for forecasting cryptocurrency prices. International Journal of Engineering, 34(1), pp.140-148.
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[18] Vadivukarassi, M., Puviarasan, N. and Aruna, P. (2017), ‘Sentimental analysis of tweets using naive bayes algorithm’,World Applied Sciences Journal 35(1), 54-59.
[19] Luo, X., Zimet, G. and Shah, S. (2019), ‘A natural language processing framework to analyse the opinions on hpv vaccination reected in twitter over 10 years (2008-2017)’, Human Vaccines and Immunotherapeutics 15(7-8), 1496.
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[23] Lee, Y., and Nelder, J. A. (1996). Hierarchical generalised linear models. Journal of the Royal Statistical Society Series B: Statistical Methodology, 58(4), 619-656.
[24] Geurts, P., Ernst, D., and Wehenkel, L. (2006). Extremely randomised trees. Machine learning, 63, 3-42.
[25] Candel, A., Parmar, V., LeDell, E., and Arora, A. (2016). Deep learning with H2O. H2O. ai Inc, 1-21.
[26] LeDell, E. E. (2015). Scalable ensemble learning and computationally efficient variance estimation. University of California, Berkeley.
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Published
2024-07-22
How to Cite
Rambevha, V. R., Sigauke, C., & Ravele, T. (2024). Predicting the closing price of cryptocurrency Ethereum. Statistics, Optimization & Information Computing, 12(5), 1306-1324. https://doi.org/10.19139/soic-2310-5070-2076
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Section
Research Articles
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