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Return Rate Prediction in Blockchain Financial Products Using Deep Learning

Author

Listed:
  • Noura Metawa

    (College of Business Administration, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
    Faculty of Commerce, Mansoura University, Mansoura 35516, Egypt)

  • Mohamemd I. Alghamdi

    (Department of Computer Science, Al-Baha University, Al-Bahah 1988, Saudi Arabia)

  • Ibrahim M. El-Hasnony

    (Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt)

  • Mohamed Elhoseny

    (Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt)

Abstract

Recently, bitcoin-based blockchain technologies have received significant interest among investors. They have concentrated on the prediction of return and risk rates of the financial product. So, an automated tool to predict the return rate of bitcoin is needed for financial products. The recently designed machine learning and deep learning models pave the way for the return rate prediction process. In this aspect, this study develops an intelligent return rate predictive approach using deep learning for blockchain financial products (RRP-DLBFP). The proposed RRP-DLBFP technique involves designing a long short-term memory (LSTM) model for the predictive analysis of return rate. In addition, Adam optimizer is applied to optimally adjust the LSTM model’s hyperparameters, consequently increasing the predictive performance. The learning rate of the LSTM model is adjusted using the oppositional glowworm swarm optimization (OGSO) algorithm. The design of the OGSO algorithm to optimize the LSTM hyperparameters for bitcoin return rate prediction shows the novelty of the work. To ensure the supreme performance of the RRP-DLBFP technique, the Ethereum (ETH) return rate is chosen as the target, and the simulation results are investigated in different measures. The simulation outcomes highlighted the supremacy of the RRP-DLBFP technique over the current state of art techniques in terms of diverse evaluation parameters. For the MSE, the proposed RRP-DLBFP has 0.0435 and 0.0655 compared to an average of 0.6139 and 0.723 for compared methods in training and testing, respectively.

Suggested Citation

  • Noura Metawa & Mohamemd I. Alghamdi & Ibrahim M. El-Hasnony & Mohamed Elhoseny, 2021. "Return Rate Prediction in Blockchain Financial Products Using Deep Learning," Sustainability, MDPI, vol. 13(21), pages 1-16, October.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:21:p:11901-:d:666496
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    References listed on IDEAS

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    1. Sifat, Imtiaz Mohammad & Mohamad, Azhar & Mohamed Shariff, Mohammad Syazwan Bin, 2019. "Lead-Lag relationship between Bitcoin and Ethereum: Evidence from hourly and daily data," Research in International Business and Finance, Elsevier, vol. 50(C), pages 306-321.
    2. Chang, Zihan & Zhang, Yang & Chen, Wenbo, 2019. "Electricity price prediction based on hybrid model of adam optimized LSTM neural network and wavelet transform," Energy, Elsevier, vol. 187(C).
    3. Alberto A. Pinto & Elvio Accinelli Gamba & Athanasios N. Yannacopoulos & Carlos Hervés-Beloso (ed.), 2016. "Trends in Mathematical Economics," Springer Books, Springer, number 978-3-319-32543-9, June.
    4. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
    5. Celeste, Valerio & Corbet, Shaen & Gurdgiev, Constantin, 2020. "Fractal dynamics and wavelet analysis: Deep volatility and return properties of Bitcoin, Ethereum and Ripple," The Quarterly Review of Economics and Finance, Elsevier, vol. 76(C), pages 310-324.
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    1. Rico-Peña, Juan Jesús & Arguedas-Sanz, Raquel & López-Martin, Carmen, 2023. "Models used to characterise blockchain features. A systematic literature review and bibliometric analysis," Technovation, Elsevier, vol. 123(C).

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