Anticipating Cryptocurrency Prices Using Machine Learning
Citations
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Cited by:
- Darío Alatorre & Carlos Gershenson & José L Mateos, 2023. "Stocks and cryptocurrencies: Antifragile or robust? A novel antifragility measure of the stock and cryptocurrency markets," PLOS ONE, Public Library of Science, vol. 18(3), pages 1-21, March.
- Abubakr Naeem, Muhammad & Iqbal, Najaf & Lucey, Brian M. & Karim, Sitara, 2022. "Good versus bad information transmission in the cryptocurrency market: Evidence from high-frequency data," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 81(C).
- 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.
- Dorien Herremans & Kah Wee Low, 2022. "Forecasting Bitcoin volatility spikes from whale transactions and CryptoQuant data using Synthesizer Transformer models," Papers 2211.08281, arXiv.org.
- Shyama Gaur & Harshit Singh & Riju Chaudhary, 2025. "Sentiment-driven forecasting: enhancing cryptocurrency price prediction with biLSTM and DistilBERT," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 16(9), pages 3198-3208, September.
- Lahmiri, Salim & Bekiros, Stelios, 2019. "Decomposing the persistence structure of Islamic and green crypto-currencies with nonlinear stepwise filtering," Chaos, Solitons & Fractals, Elsevier, vol. 127(C), pages 334-341.
- Ze Shen & Qing Wan & David J. Leatham, 2021. "Bitcoin Return Volatility Forecasting: A Comparative Study between GARCH and RNN," JRFM, MDPI, vol. 14(7), pages 1-18, July.
- Carolina E S Mattsson & Teodoro Criscione & Frank W Takes, 2022. "Circulation of a digital community currency," Papers 2207.08941, arXiv.org, revised Jun 2023.
- Chowdhury, Reaz & Rahman, M. Arifur & Rahman, M. Sohel & Mahdy, M.R.C., 2020. "An approach to predict and forecast the price of constituents and index of cryptocurrency using machine learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
- Sevcan Uzun & Ahmet Sensoy & Duc Khuong Nguyen, 2023. "Jump forecasting in foreign exchange markets: A high‐frequency analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 578-624, April.
- Andrés García-Medina & Graciela González Farías, 2020. "Transfer entropy as a variable selection methodology of cryptocurrencies in the framework of a high dimensional predictive model," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-31, January.
- Ahmed M. Khedr & Ifra Arif & Pravija Raj P V & Magdi El‐Bannany & Saadat M. Alhashmi & Meenu Sreedharan, 2021. "Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 28(1), pages 3-34, January.
- Abeer ElBahrawy & Laura Alessandretti & Andrea Baronchelli, 2019. "Wikipedia and Digital Currencies: Interplay Between Collective Attention and Market Performance," Papers 1902.04517, arXiv.org, revised Mar 2019.
- Levantesi, Susanna & Piscopo, Gabriella & Roviello, Alba, 2025. "Cryptocurrency in global dynamics: Analyzing the Crypto Volatility Index and financial markets with machine learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 674(C).
- Wang, Yaqi & Wang, Chunfeng & Sensoy, Ahmet & Yao, Shouyu & Cheng, Feiyang, 2022. "Can investors’ informed trading predict cryptocurrency returns? Evidence from machine learning," Research in International Business and Finance, Elsevier, vol. 62(C).
- D’Amato, Valeria & Levantesi, Susanna & Piscopo, Gabriella, 2022. "Deep learning in predicting cryptocurrency volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
- Ren, Yi-Shuai & Ma, Chao-Qun & Kong, Xiao-Lin & Baltas, Konstantinos & Zureigat, Qasim, 2022. "Past, present, and future of the application of machine learning in cryptocurrency research," Research in International Business and Finance, Elsevier, vol. 63(C).
- Jacopo Fior & Luca Cagliero & Paolo Garza, 2022. "Leveraging Explainable AI to Support Cryptocurrency Investors," Future Internet, MDPI, vol. 14(9), pages 1-19, August.
- Duygu Ider & Stefan Lessmann, 2022. "Forecasting Cryptocurrency Returns from Sentiment Signals: An Analysis of BERT Classifiers and Weak Supervision," Papers 2204.05781, arXiv.org, revised Mar 2023.
- Gil Cohen, 2022. "Algorithmic Trading and Financial Forecasting Using Advanced Artificial Intelligence Methodologies," Mathematics, MDPI, vol. 10(18), pages 1-13, September.
- Silvia Bartolucci & Fabio Caccioli & Pierpaolo Vivo, 2019. "A percolation model for the emergence of the Bitcoin Lightning Network," Papers 1912.03556, arXiv.org.
- Suhwan Ji & Jongmin Kim & Hyeonseung Im, 2019. "A Comparative Study of Bitcoin Price Prediction Using Deep Learning," Mathematics, MDPI, vol. 7(10), pages 1-20, September.
- J. Gavin & M. Crane, 2021. "Community Detection in Cryptocurrencies with Potential Applications to Portfolio Diversification," Papers 2108.09763, arXiv.org.
- Isabela Ruiz Roque da Silva & Eli Hadad Junior & Pedro Paulo Balbi, 2022. "Cryptocurrencies trading algorithms: A review," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1661-1668, December.
- 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.
- Lahmiri, Salim & Bekiros, Stelios, 2020. "Big data analytics using multi-fractal wavelet leaders in high-frequency Bitcoin markets," Chaos, Solitons & Fractals, Elsevier, vol. 131(C).
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