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Modeling and Forecasting Cryptocurrency Closing Prices with Rao Algorithm-Based Artificial Neural Networks: A Machine Learning Approach

Author

Listed:
  • Sanjib Kumar Nayak

    (Department of Computer Application, VSS University of Technology, Burla, Sambalpur 768018, India)

  • Sarat Chandra Nayak

    (Department of Artificial Intelligence and Machine Learning, CMR College of Engineering & Technology, Hyderabad 501401, India)

  • Subhranginee Das

    (Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation (KL University), Hyderabad 500075, India)

Abstract

Artificial neural networks (ANNs) are suitable procedures for predicting financial time series (FTS). Cryptocurrencies are good investment assets; therefore, the effective prediction of cryptocurrencies has become a trending area of research. Capturing inherent uncertainties associated with cryptocurrency FTS with conventional methods is difficult. Though ANNs are the better alternative, fixing the optimal parameters of ANNs is a tedious job. This article develops a hybrid ANN through Rao algorithm (RA + ANN) for the effective prediction of six popular cryptocurrencies such as Bitcoin, Litecoin, Ethereum, CMC 200, Tether, and Ripple. Six comparative models such as GA + ANN, PSO + ANN, MLP, SVM, LSE, and ARIMA are developed and trained in a similar way. All these models are evaluated through the mean absolute percentage of error (MAPE) and average relative variance (ARV) metrics. It is found that the proposed RA + ANN generated the lowest MAPE and ARV values, statistically different as compared with existing methods mentioned above, and hence can be recommended as a potential financial instrument for predicting cryptocurrencies.

Suggested Citation

  • Sanjib Kumar Nayak & Sarat Chandra Nayak & Subhranginee Das, 2021. "Modeling and Forecasting Cryptocurrency Closing Prices with Rao Algorithm-Based Artificial Neural Networks: A Machine Learning Approach," FinTech, MDPI, vol. 1(1), pages 1-16, December.
    • Handle: RePEc:gam:jfinte:v:1:y:2021:i:1:p:4-62:d:714689
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    References listed on IDEAS

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