Version 1
: Received: 26 February 2024 / Approved: 27 February 2024 / Online: 27 February 2024 (12:25:51 CET)
How to cite:
Ravele, T.; Sigauke, C.; Rambevha, V. R. Predicting Closing Price of Cryptocurrency Ethereum. Preprints2024, 2024021537. https://doi.org/10.20944/preprints202402.1537.v1
Ravele, T.; Sigauke, C.; Rambevha, V. R. Predicting Closing Price of Cryptocurrency Ethereum. Preprints 2024, 2024021537. https://doi.org/10.20944/preprints202402.1537.v1
Ravele, T.; Sigauke, C.; Rambevha, V. R. Predicting Closing Price of Cryptocurrency Ethereum. Preprints2024, 2024021537. https://doi.org/10.20944/preprints202402.1537.v1
APA Style
Ravele, T., Sigauke, C., & Rambevha, V. R. (2024). Predicting Closing Price of Cryptocurrency Ethereum. Preprints. https://doi.org/10.20944/preprints202402.1537.v1
Chicago/Turabian Style
Ravele, T., Caston Sigauke and Vhukhudo Ronny Rambevha. 2024 "Predicting Closing Price of Cryptocurrency Ethereum" Preprints. https://doi.org/10.20944/preprints202402.1537.v1
Abstract
Considering that cryptocurrencies are now present in practically every financial transaction because they are widely accepted as an alternate means of making payments and exchanging currencies, academics and economists have more opportunities to study cryptocurrency prices. Over the years, investors, traders and investment banks have found it difficult to predict the closing daily price of Ethereum due to its rapid price fluctuation. The daily closing price of cryptocurrency is essential to consider when trading or investing in Ethereum. This report focuses on carrying out a comparative study of the predictive capabilities of deep machine learning algorithms with a stacking ensemble modelling framework using daily historical observations of the price of Ethereum obtained from Coindesk, tweets extracted from Twitter ranging from the 1st of August 2022 to the 8th of August 2022 and other five covariates (closing price lag1, closing price lag2, noltrend, daytype and month) engineered from the closing price of Ethereum. Seven models are used to compute the forecasts for the daily closing price of Ethereum; these are the recurrent neural network, ensemble stacked recurrent neural network, gradient boosting machine, generalized linear model, distributed random forest, deep neural networks and stacked ensemble for gradient boosting machine, generalized linear model, distributed random forest and deep neural networks. The main evaluation metric used is the mean absolute error. According to MAE, RNN forecasts outperform the other model’s forecasts in this study, producing an MAE of 0.0309.
Computer Science and Mathematics, Probability and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
