Forecasting cryptocurrency returns with machine learning
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DOI: 10.1016/j.ribaf.2023.101905
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Cited by:
- Vecchi, Edoardo & Berra, Gabriele & Albrecht, Steffen & Gagliardini, Patrick & Horenko, Illia, 2023. "Entropic approximate learning for financial decision-making in the small data regime," Research in International Business and Finance, Elsevier, vol. 65(C).
- Caparrini, Antonio & Arroyo, Javier & Escayola Mansilla, Jordi, 2024. "S&P 500 stock selection using machine learning classifiers: A look into the changing role of factors," Research in International Business and Finance, Elsevier, vol. 70(PA).
- Riahi, Rabeb & Bennajma, Amel & Jahmane, Abderrahmane & Hammami, Helmi, 2024. "Investing in cryptocurrency before and during the COVID-19 crisis: Hedge, diversifier or safe haven?," Research in International Business and Finance, Elsevier, vol. 67(PB).
- Nagl, Maximilian, 2024. "Intricacy of cryptocurrency returns," Economics Letters, Elsevier, vol. 239(C).
- Jirou, Ismail & Jebabli, Ikram & Lahiani, Amine, 2025. "A hybrid deep learning model for cryptocurrency returns forecasting: Comparison of the performance of financial markets and impact of external variables," Research in International Business and Finance, Elsevier, vol. 73(PA).
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More about this item
Keywords
Cryptocurrency; Machine learning; eXtreme Gradient Boostine; SHapley Additive exPlanations;All these keywords.
JEL classification:
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
- G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
- G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
- G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
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