Forecasting of Cryptocurrency Price and Financial Stability: Fresh Insights based on Big Data Analytics and Deep Learning Artificial Intelligence Techniques
Received: 17 February 2024 | Revised: 22 March 2024 | Accepted: 6 April 2024 | Online: 1 June 2024
Corresponding author: Kais Tissaoui
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, forecastingDownloads
References
S. A. Gyamerah, "On forecasting the intraday Bitcoin price using ensemble of variational mode decomposition and generalized additive model," Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 3, pp. 1003–1009, Mar. 2022.
R. K. Jana, I. Ghosh, and D. Das, "A differential evolution-based regression framework for forecasting Bitcoin price," Annals of Operations Research, vol. 306, no. 1, pp. 295–320, Nov. 2021.
P. Katsiampa, "Volatility estimation for Bitcoin: A comparison of GARCH models," Economics Letters, vol. 158, pp. 3–6, Sep. 2017.
K. Tissaoui, T. Zaghdoudi, and K. I. Alfreahat, "Can intraday public information explain Bitcoin Returns and Volatility? A PGARCH-Based Approach," Economics Bulletin, vol. 40, no. 3, pp. 2085–2092, 2020.
A. Azari, "Bitcoin Price Prediction: An ARIMA Approach." arXiv, Apr. 04, 2019.
I. Ghosh, M. K. Sanyal, and R. K. Jana, "Analysis of Causal Interactions and Predictive Modelling of Financial Markets Using Econometric Methods, Maximal Overlap Discrete Wavelet Transformation and Machine Learning: A Study in Asian Context," in Pattern Recognition and Machine Intelligence, 2017, pp. 664–672.
A. C. da Silva Filho, N. D. Maganini, and E. F. de Almeida, "Multifractal analysis of Bitcoin market," Physica A: Statistical Mechanics and its Applications, vol. 512, pp. 954–967, Dec. 2018.
S. Lahmiri and S. Bekiros, "Cryptocurrency forecasting with deep learning chaotic neural networks," Chaos, Solitons & Fractals, vol. 118, pp. 35–40, Jan. 2019.
J. Brownlee, Deep Learning for Time Series Forecasting: Predict the Future with MLPs, CNNs and LSTMs in Python. Machine Learning Mastery, 2018.
H. Ben Ameur, S. Boubaker, Z. Ftiti, W. Louhichi, and K. Tissaoui, "Forecasting commodity prices: empirical evidence using deep learning tools," Annals of Operations Research, Jan. 2023.
Z. Alshingiti, R. Alaqel, J. Al-Muhtadi, Q. E. U. Haq, K. Saleem, and M. H. Faheem, "A Deep Learning-Based Phishing Detection System Using CNN, LSTM, and LSTM-CNN," Electronics, vol. 12, no. 1, Jan. 2023, Art. no. 232.
C. Jin and Y. Li, "Cryptocurrency Price Prediction Using Frequency Decomposition and Deep Learning," Fractal and Fractional, vol. 7, no. 10, Oct. 2023, Art. no. 708.
F. Aksan, Y. Li, V. Suresh, and P. Janik, "CNN-LSTM vs. LSTM-CNN to Predict Power Flow Direction: A Case Study of the High-Voltage Subnet of Northeast Germany," Sensors, vol. 23, no. 2, Jan. 2023, Art. no. 901.
J. S. Kamdem, R. B. Essomba, and J. N. Berinyuy, "Deep learning models for forecasting and analyzing the implications of COVID-19 spread on some commodities markets volatilities," Chaos, Solitons & Fractals, vol. 140, Nov. 2020, Art. no. 110215.
M. Liu, G. Li, J. Li, X. Zhu, and Y. Yao, "Forecasting the price of Bitcoin using deep learning," Finance Research Letters, vol. 40, May 2021, Art. no. 101755.
A. Vidal and W. Kristjanpoller, "Gold volatility prediction using a CNN-LSTM approach," Expert Systems with Applications, vol. 157, Nov. 2020, Art. no. 113481.
Z. Alameer, A. Fathalla, K. Li, H. Ye, and Z. Jianhua, "Multistep-ahead forecasting of coal prices using a hybrid deep learning model," Resources Policy, vol. 65, Mar. 2020, Art. no. 101588.
Z. Alshingiti, R. Alaqel, J. Al-Muhtadi, Q. E. U. Haq, K. Saleem, and M. H. Faheem, "A Deep Learning-Based Phishing Detection System Using CNN, LSTM, and LSTM-CNN," Electronics, vol. 12, no. 1, Jan. 2023, Art. no. 232.
M. Gabriel and T. Auer, "LSTM Deep Learning Models for Virtual Sensing of Indoor Air Pollutants: A Feasible Alternative to Physical Sensors," Buildings, vol. 13, no. 7, Jul. 2023, Art. no. 1684.
S. Tanwar, N. Patel, S. Patel, J. Patel, and I. Davidson, "Deep Learning-Based Cryptocurrency Price Prediction Scheme With Inter-Dependent Relations," IEEE Access, Vol. 9, Oct. 2021.
H. F. Nematallah, A. A. H. Sedky, and K. M. Mahar, "Bitcoin Price Trend Prediction Using Deep Neural Network," Webology, vol. 19, no. 3, pp. 2652–2655, 2022.
F. Ferdiansyah, S. H. Othman, R. Z. M. Radzi, D. Stiawan, and T. Sutikno, "Hybrid gated recurrent unit bidirectional-long short-term memory model to improve cryptocurrency prediction accuracy," IAES International Journal of Artificial Intelligence (IJ-AI), vol. 12, no. 1, pp. 251–261, Mar. 2023.
R. Murugesan, V. Shanmugaraja, and A. Vadivel, "Forecasting Bitcoin Price Using Interval Graph and ANN Model: A Novel Approach," SN Computer Science, vol. 3, no. 5, Aug. 2022, Art. no. 411.
C. Y. Kang, C. P. Lee, and K. M. Lim, "Cryptocurrency Price Prediction with Convolutional Neural Network and Stacked Gated Recurrent Unit," Data, vol. 7, no. 11, Nov. 2022, Art. no. 149.
S. A. Basher and P. Sadorsky, "Forecasting Bitcoin price direction with random forests: How important are interest rates, inflation, and market volatility?," Machine Learning with Applications, vol. 9, Sep. 2022, Art. no. 100355.
P. L. Seabe, C. R. B. Moutsinga, and E. Pindza, "Forecasting Cryptocurrency Prices Using LSTM, GRU, and Bi-Directional LSTM: A Deep Learning Approach," Fractal and Fractional, vol. 7, no. 2, Feb. 2023, Art. no. 203.
K. Murray, A. Rossi, D. Carraro, and A. Visentin, "On Forecasting Cryptocurrency Prices: A Comparison of Machine Learning, Deep Learning, and Ensembles," Forecasting, vol. 5, no. 1, pp. 196–209, Mar. 2023.
S. Boubaker, M. Benghanem, A. Mellit, A. Lefza, O. Kahouli, and L. Kolsi, "Deep Neural Networks for Predicting Solar Radiation at Hail Region, Saudi Arabia," IEEE Access, vol. 9, pp. 36719–36729, 2021.
J. Lago, F. De Ridder, and B. De Schutter, "Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms," Applied Energy, vol. 221, pp. 386–405, Jul. 2018.
K. Tissaoui, "Forecasting implied volatility risk indexes: International evidence using Hammerstein-ARX approach," International Review of Financial Analysis, vol. 64, pp. 232–249, Jul. 2019.
Z. Ftiti, K. Tissaoui, and S. Boubaker, "On the relationship between oil and gas markets: a new forecasting framework based on a machine learning approach," Annals of Operations Research, vol. 313, no. 2, pp. 915–943, Jun. 2022.
G. Memarzadeh and F. Keynia, "Short-term electricity load and price forecasting by a new optimal LSTM-NN based prediction algorithm," Electric Power Systems Research, vol. 192, Mar. 2021, Art. no. 106995.
O. M. Ahmed, L. M. Haji, A. M. Ahmed, and N. M. Salih, "Bitcoin Price Prediction using the Hybrid Convolutional Recurrent Model Architecture," Engineering, Technology & Applied Science Research, vol. 13, no. 5, pp. 11735–11738, Oct. 2023.
H. Rana, M. U. Farooq, A. K. Kazi, M. A. Baig, and M. A. Akhtar, "Prediction of Agricultural Commodity Prices using Big Data Framework," Engineering, Technology & Applied Science Research, vol. 14, no. 1, pp. 12652–12658, Feb. 2024.
Downloads
How to Cite
License
Copyright (c) 2024 Jihen Bouslimi, Sahbi Boubaker, Kais Tissaoui
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
