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N-BEATS Perceiver: A Novel Approach for Robust Cryptocurrency Portfolio Forecasting

Author

Listed:
  • Attilio Sbrana

    (Aeronautics Institute of Technology (ITA))

  • Paulo André Lima de Castro

    (Aeronautics Institute of Technology (ITA))

Abstract

In this paper, we propose a novel approach for forecasting cryptocurrency portfolios, harnessing modified versions of the N-BEATS deep learning architecture, integrated with convolutional network layers, Transformer mechanisms, and the Mish activation function. Our thorough evaluation, featuring an extensive sample size exceeding 4 million portfolio test samples, shows these variations outperforming traditional and other deep learning forecasting methods across various metrics. Particularly noteworthy is our N-BEATS Perceiver model, a Transformer-based variation, which not only delivers superior forecast accuracy but also exhibits a robust risk profile with less downside. Furthermore, the model performs exceptionally well under the TOPSIS method across a broad spectrum of portfolio evaluation parameters, making it a valuable asset for both portfolio selection and risk management in the dynamic cryptocurrency market.

Suggested Citation

  • Attilio Sbrana & Paulo André Lima de Castro, 2024. "N-BEATS Perceiver: A Novel Approach for Robust Cryptocurrency Portfolio Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 64(2), pages 1047-1081, August.
    • Handle: RePEc:kap:compec:v:64:y:2024:i:2:d:10.1007_s10614-023-10470-8
    DOI: 10.1007/s10614-023-10470-8
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    References listed on IDEAS

    as
    1. Georgios Tzagkarakis & Frantz Maurer, 2022. "Horizon-Adaptive Extreme Risk Quantification for Cryptocurrency Assets," Post-Print hal-03953953, HAL.
    2. Stephen Chan & Saralees Nadarajah, 2019. "Risk: An R Package for Financial Risk Measures," Computational Economics, Springer;Society for Computational Economics, vol. 53(4), pages 1337-1351, April.
    3. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & David Martinez-Rego & Fan Wu & Lingbo Li, 2022. "Cryptocurrency trading: a comprehensive survey," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-59, December.
    4. Chuen Yik Kang & Chin Poo Lee & Kian Ming Lim, 2022. "Cryptocurrency Price Prediction with Convolutional Neural Network and Stacked Gated Recurrent Unit," Data, MDPI, vol. 7(11), pages 1-13, October.
    5. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & Lingbo Li & David Martinez-Regoband & Fan Wu, 2020. "Cryptocurrency Trading: A Comprehensive Survey," Papers 2003.11352, arXiv.org, revised Jan 2022.
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