Forecasting of Cryptocurrency Price and Financial Stability: Fresh Insights based on Big Data Analytics and Deep Learning Artificial Intelligence Techniques

Authors

  • Jihen Bouslimi College of Administrative and Financial Sciences, Saudi Electronic University, Saudi Arabia
  • Sahbi Boubaker Department of Computer and Network Engineering, College of Computer Science and Engineering, University of Jeddah, Saudi Arabia
  • Kais Tissaoui Management Information Systems Department, Applied College, University of Ha’il, Saudi Arabia
Volume: 14 | Issue: 3 | Pages: 14162-14169 | June 2024 | https://doi.org/10.48084/etasr.7096

Abstract

This paper evaluates the performance of the Long Short-Term Memory (LSTM) deep learning algorithm in forecasting Bitcoin and Ethereum prices during the COVID-19 epidemic, using their high-frequency price information, ranging from December 31, 2019, to December 31, 2020. Deep learning (DL) techniques, which can withstand stylized facts, such as non-linearity and long-term memory in high-frequency data, were utilized in this paper. The LSTM algorithm was employed due to its ability to perform well with time series data by reducing fading gradients and reliance over time. The obtained empirical results demonstrate that the LSTM technique can predict both Ethereum and Bitcoin prices. However, the performance of this algorithm decreases as the number of hidden units and epochs grows, with 100 hidden units and 200 epochs delivering maximum forecast accuracy. Furthermore, the performance study demonstrates that the LSTM approach gives more accurate forecasts for Ethereum than for Bitcoin prices, indicating that Ethereum is more prominent than Bitcoin. Moreover, the increased accuracy of forecasting the Ethereum price made it more reliable than Bitcoin during the COVID-19 coronavirus crisis. As a result, cryptocurrency traders might focus on trading Ethereum to increase their earnings during a crisis.

Keywords:

cryptocurrency prices, COVID-19 pandemic, high-frequency data, LSTM approach, forecasting

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How to Cite

[1]
J. Bouslimi, S. Boubaker, and K. Tissaoui, “Forecasting of Cryptocurrency Price and Financial Stability: Fresh Insights based on Big Data Analytics and Deep Learning Artificial Intelligence Techniques”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 3, pp. 14162–14169, Jun. 2024.

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