Knowledge Economy Studies

Evaluation of the Performance of Deep Learning Models in Cryptocurrency Price Prediction: A Case Study of Bitcoin, Dogecoin, Ethereum, and Ripple

Document Type : Original Article

Authors

Reza Taleblou
Parisa Mohajeri

Associate Professor of Economics, Department of Theoretical Economics, Allameh Tabataba'i University, Tehran, Iran.

https://doi.org/10.22034/kes.2025.2054189.1046
Abstract
Cryptocurrencies, as one of the emerging asset classes, have gained significant popularity in recent years. Accurate forecasting of cryptocurrencies’ prices has become highly attractive for both researchers and investors due to their volatile and non-linear price behavior. However, predicting the cryptocurrencies’ prices accurately remains challenging due to their substantial fluctuations and complex dynamics. Research findings indicated that the methods of deep learning and neural networks outperform traditional econometric approaches in forecasting financial and economic time series. Among the techniques of neural network and deep learning, various types of Recurrent Neural Network (RNN) models have been proven to be effective. This study employed three Recurrent Neural Network architectures—RNN, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)—to predict the logarithm of the prices of four major cryptocurrencies of Bitcoin (BTC), Dogecoin (DOGE), Ethereum (ETH), and Ripple (XRP). Daily time-series data from January 17, 2018, to December 18, 2024, were utilized for this purpose. The data were collected using the cryptocmd python package. The experimental results which were assessed using four metrics—Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Symmetric Mean Absolute Percentage Error (SMAPE)—revealed two key findings: First, as the forecasting horizon increases, the required input size for achieving the best predictions increases for all models. Second, the LSTM model demonstrates a superior performance in predicting the prices of major cryptocurrencies for 1-day and 30-day horizons, whereas the GRU model exhibits the lowest prediction error for a 7-day horizon. These findings provided valuable insights for estimating the mean equation, which is instrumental in forecasting the expected returns of cryptocurrency assets for risk management purposes.

Keywords

Artificial Intelligence
Cryptocurrencies
Deep Learning
Gated Recurrent Units
Long Short-Term Memory
Recurrent Neural Networks

Subjects

Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Networks, 5(2), 157–166.
Catania, L., Grassi, S., & Ravazzolo, F. (2019). Forecasting cryptocurrencies under model and parameter instability. International Journal of Forecasting, 35(2), 485–501.
Che, Z., Purushotham, S., Cho, K., Sontag, D., & Liu, Y. (2018). Recurrent neural networks for multivariate time series with missing values. Scientific Reports, 8(1), 6085, 1-12.
Chen, W., Xu, H., Jia, L., & Gao, Y. (2021). Machine learning model for bitcoin exchange rate prediction using economic and technology determinants. International Journal of Forecasting, 37(1), 28–43.
Chen, X., Wei, L., & Xu, J. (2017). House price prediction using LSTM. arXiv preprint arXiv:1709.08432.
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.
Conrad, C., Custovic, A., & Ghysels, E. (2018). Long-and short-term cryptocurrency volatility components: A GARCH-MIDAS analysis. Journal of Risk and Financial Management, 11(2), 23.
Dong, F., Xu, Z., & Zhang, Y. (2022). Bubbly Bitcoin. Economic Theory, 74(3), 9731015.
Fang, T., Su, Z., & Yin, L. (2020). Economic fundamentals or investor perceptions? The role of uncertainty in predicting long-term cryptocurrency volatility. International Review of Financial Analysis, 71, 101566.
Fischer, T., & Krauss, C. (2018) Deep learning with long short-term memory networks for financial market predictions, European journal of operational research, 270 (2), 654-669.
Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning (Vol. 1, No. 2). Cambridge: MIT press.
Graves, A. (2012). Sequence transduction with recurrent neural networks. ArXiv preprint arXiv: 1211.3711.
Hamayel, M. J., & Owda, A. Y. (2021). A novel cryptocurrency price prediction model using GRU, LSTM and bi-LSTM machine learning algorithms. AI, 2(4), 477–496.
Hansson, F., & Rostami, J. (2019). Time series forecasting of house prices: an evaluation of a support vector machine and a recurrent neural network with LSTM ells. Uppsala University Thesis. May 2019.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
Ibrahim, A., Kashef, R., & Corrigan, L. (2021). Predicting market movement direction for Bitcoin: A comparison of time series modeling methods. Computers and Electrical Engineering, 89, 106905.
Jozefowicz, R., Zaremba, W., & Sutskever, I. (2015, June). An empirical exploration of recurrent network architectures. In International conference on machine learning (pp. 2342-2350). PMLR.
Klein, T., Thu, H. P., & Walther, T. (2018). Bitcoin is not the new gold–a comparison of volatility, correlation, and portfolio performance. International Review of Financial Analysis, 59, 105–116.
Kristjanpoller, W., & Minutolo, M. C. (2018). A hybrid volatility forecasting framework integrating GARCH, artificial neural network, technical analysis and principal components analysis. Expert Systems with Applications, 109, 1–11.
Kubat, M. (2015).Virtual currency Bitcoin in the scope of money definition and store of value. Procedia Economics and Finance, 30, 409–416. https://doi.org/10.1016/S2212-5671(15)01308-8.
Kyriazis, N., Papadamou, S., & Corbet, S. (2020). A systematic review of the bubble dynamics of cryptocurrency prices. Research in International Business and Finance, 54, 101254.
Lahmiri, S., & Bekiros, S. (2019). Cryptocurrency forecasting with deep learning chaotic neural networks. Chaos, Solitons & Fractals, 118, 35–40.
Li, Y., & Dai, W. (2020). Bitcoin price forecasting method based on CNN- LSTM hybrid neural network model. The Journal of Engineering, 2020(13), 344–347.
Limsombunchai, V., Gan Ch., & Lee, M. (2004). House price prediction: hedonic price model vs. ANN. American Journal of Applied Sciences, 1 (3), 193-201.
Nakano, M., Takahashi, A., & Takahashi, S. (2018). Bitcoin technical trading with artificial neural network. Physica A: Statistical Mechanics and its Applications, 510, 587609.
Parekh, R., Patel, N. P., Thakkar, N., Gupta, R., Tanwar, S., Sharma, G., ... & Sharma, R. (2022). DL-GuesS: Deep learning and sentiment analysis-based cryptocurrency price prediction. IEEE Access, 10, 35398-35409.
Patel, M. M., Tanwar, S., Gupta, R., & Kumar, N. (2020) A deep learning-based cryptocurrency price prediction scheme for financial institutions. Journal of Information Security and Applications, 55, 102583.
Sayadi Nezhad, S., Esmaeilzadeh Maghari, A., & Rostami, M.R. (2023). Presenting the forecasting model of Bitcoin return using the hybrid method of deep learning-signal decomposition algorithm (CEEMD-DL). Journal of Financial Economics, 17(1), 217-238 (In Persian).
Seabe, P. L., Moutsinga, C., & Pindza, E. (2023). Forecasting cryptocurrency prices using LSTM, GRU, and Bi-Directional LSTM: A deep learning approach. Fractal Fractional, 7(2), 203, 7020203.
Sezer, O. B., Gudelek, M. U., & Ozbayoglu, A. M. (2020). Financial time series forecasting with deep learning: A systematic literature review: 2005– 2019, Applied soft computing, 90, 106181.
Song, Y. (2018). Stock trend prediction: Based on machine learning methods (Doctoral dissertation, UCLA).
Tsay R. S. (2005). Analysis of financial time series, John Wiley & Sons.
Walther, T., Klein, T., & Bouri, E. (2019). Exogenous drivers of Bitcoin and cryptocurrency volatility–a mixed data sampling approach to forecasting. Journal of International Financial Markets Institutions and Money, 63, 101133.
Xu, L., Li, C., Xie, X., & Zhang, G. (2018). Long short-term memory network based hybrid model for short-term electrical load forecasting. Information, 9(7), 165.
Zhang, W., Wang, P., Li, X., & Shen, D. (2018). Some stylized facts of the cryptocurrency market. Applied Economics, 50(55), 5950–5965.
Zhang, Z., Dai, H. N., Zhou, J., Mondal, S. K., García, M. M., & Wang, H. (2021). Forecasting cryptocurrency price using convolutional neural networks with weighted and attentive memory channels. Expert Systems with Applications, 183, 115378.
Knowledge Economy Studies
Volume 2, Issue 1
April 2025
Pages 7-19

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