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Accurate, Secure and Explainable bitcoin forecasting

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  • Bagheri, Maryamsadat
  • Giudici, Paolo

Abstract

Forecasting the price of bitcoin assets is a difficult task, especially as bitcoins are highly volatile and speculative. In this paper we leverage the non linear capability of deep and machine learning models to enhance bitcoin forecasts. We propose a systematic comparison of different deep learning and machine learning models, based on their Accuracy, Security and Explainability characteristics. The empirical findings reveal that, while CNN–GRU, GRU and LSTM are the most accurate models, for maximum cumulative return and risk adjusted performance GRU and CNN are preferred. Whereas, for transparent and stable decision-making, Random Forest and XGboost are a good choice and, for robustness, CNN and LSTM are the best choice. Ultimately, the choice of a model depends on the objectives of the analysis.

Suggested Citation

  • Bagheri, Maryamsadat & Giudici, Paolo, 2025. "Accurate, Secure and Explainable bitcoin forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 678(C).
    • Handle: RePEc:eee:phsmap:v:678:y:2025:i:c:s0378437125006260
    DOI: 10.1016/j.physa.2025.130974
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    References listed on IDEAS

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    1. Paolo Giudici & Emanuela Raffinetti, 2025. "RGA: a unified measure of predictive accuracy," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 19(1), pages 67-93, March.
    2. Di Wu, 2025. "Institutional Adoption and Correlation Dynamics: Bitcoin's Evolving Role in Financial Markets," Papers 2501.09911, arXiv.org.
    3. Giudici, Paolo & Raffinetti, Emanuela, 2023. "SAFE Artificial Intelligence in finance," Finance Research Letters, Elsevier, vol. 56(C).
    4. Goodell, John W. & Ben Jabeur, Sami & Saâdaoui, Foued & Nasir, Muhammad Ali, 2023. "Explainable artificial intelligence modeling to forecast bitcoin prices," International Review of Financial Analysis, Elsevier, vol. 88(C).
    5. Tiago E. Pratas & Filipe R. Ramos & Lihki Rubio, 2023. "Forecasting bitcoin volatility: exploring the potential of deep learning," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 13(2), pages 285-305, June.
    6. Mohammad Ali Labbaf Khaniki & Mohammad Manthouri, 2024. "Enhancing Price Prediction in Cryptocurrency Using Transformer Neural Network and Technical Indicators," Papers 2403.03606, arXiv.org.
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