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Prediction of cryptocurrency returns using machine learning

Citations

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Cited by:

  1. Dante Miller & Jong-Min Kim, 2021. "Univariate and Multivariate Machine Learning Forecasting Models on the Price Returns of Cryptocurrencies," JRFM, MDPI, vol. 14(10), pages 1-10, October.
  2. Hakan Pabuccu & Adrian Barbu, 2023. "Feature Selection with Annealing for Forecasting Financial Time Series," Papers 2303.02223, arXiv.org, revised Feb 2024.
  3. Erdinc Akyildirim & Oguzhan Cepni & Shaen Corbet & Gazi Salah Uddin, 2023. "Forecasting mid-price movement of Bitcoin futures using machine learning," Annals of Operations Research, Springer, vol. 330(1), pages 553-584, November.
  4. Julien Chevallier & Dominique Guégan & Stéphane Goutte, 2021. "Is It Possible to Forecast the Price of Bitcoin?," Forecasting, MDPI, vol. 3(2), pages 1-44, May.
  5. 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).
  6. Praveen Puram & Soumya Roy & Deepak Srivastav & Anand Gurumurthy, 2023. "Understanding the effect of contextual factors and decision making on team performance in Twenty20 cricket: an interpretable machine learning approach," Annals of Operations Research, Springer, vol. 325(1), pages 261-288, June.
  7. 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.
  8. 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).
  9. Zhang, Dongna & Chen, Xihui Haviour & Lau, Chi Keung Marco & Xu, Bing, 2023. "Implications of cryptocurrency energy usage on climate change," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
  10. Banerjee, Ameet Kumar & Akhtaruzzaman, Md & Dionisio, Andreia & Almeida, Dora & Sensoy, Ahmet, 2022. "Nonlinear nexus between cryptocurrency returns and COVID-19 news sentiment," Journal of Behavioral and Experimental Finance, Elsevier, vol. 36(C).
  11. Yae, James & Tian, George Zhe, 2022. "Out-of-sample forecasting of cryptocurrency returns: A comprehensive comparison of predictors and algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
  12. Nagula, Pavan Kumar & Alexakis, Christos, 2022. "A new hybrid machine learning model for predicting the bitcoin (BTC-USD) price," Journal of Behavioral and Experimental Finance, Elsevier, vol. 36(C).
  13. Cohen, Gil & Aiche, Avishay, 2023. "Forecasting gold price using machine learning methodologies," Chaos, Solitons & Fractals, Elsevier, vol. 175(P2).
  14. Jiqian Wang & Feng Ma & Elie Bouri & Yangli Guo, 2023. "Which factors drive Bitcoin volatility: Macroeconomic, technical, or both?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 970-988, July.
  15. Gyana Ranjan Patra & Mihir Narayan Mohanty, 2023. "Price Prediction of Cryptocurrency Using a Multi-Layer Gated Recurrent Unit Network with Multi Features," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1525-1544, December.
  16. Yue, Yao & Li, Xuerong & Zhang, Dingxuan & Wang, Shouyang, 2021. "How cryptocurrency affects economy? A network analysis using bibliometric methods," International Review of Financial Analysis, Elsevier, vol. 77(C).
  17. Mingzhe Wei & Georgios Sermpinis & Charalampos Stasinakis, 2023. "Forecasting and trading Bitcoin with machine learning techniques and a hybrid volatility/sentiment leverage," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 852-871, July.
  18. Soria, Jorge & Moya, Jorge & Mohazab, Amin, 2023. "Optimal mining in proof-of-work blockchain protocols," Finance Research Letters, Elsevier, vol. 53(C).
  19. Pho, Kim Hung & Ly, Sel & Lu, Richard & Hoang, Thi Hong Van & Wong, Wing-Keung, 2021. "Is Bitcoin a better portfolio diversifier than gold? A copula and sectoral analysis for China," International Review of Financial Analysis, Elsevier, vol. 74(C).
  20. Tran, Quang Van & Kukal, Jaromir, 2022. "A novel heavy tail distribution of logarithmic returns of cryptocurrencies," Finance Research Letters, Elsevier, vol. 47(PA).
  21. 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.
  22. Babaei, Golnoosh & Giudici, Paolo & Raffinetti, Emanuela, 2022. "Explainable artificial intelligence for crypto asset allocation," Finance Research Letters, Elsevier, vol. 47(PB).
  23. 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).
  24. Rasoul Amirzadeh & Asef Nazari & Dhananjay Thiruvady & Mong Shan Ee, 2023. "Causal Feature Engineering of Price Directions of Cryptocurrencies using Dynamic Bayesian Networks," Papers 2306.08157, arXiv.org, revised Apr 2024.
  25. Liu, Yujun & Li, Zhongfei & Nekhili, Ramzi & Sultan, Jahangir, 2023. "Forecasting cryptocurrency returns with machine learning," Research in International Business and Finance, Elsevier, vol. 64(C).
  26. Bernd Süssmuth, 2022. "The mutual predictability of Bitcoin and web search dynamics," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 435-454, April.
  27. Tan, Xilong & Tao, Yubo, 2023. "Trend-based forecast of cryptocurrency returns," Economic Modelling, Elsevier, vol. 124(C).
  28. Timothy King & Dimitrios Koutmos, 2021. "Herding and feedback trading in cryptocurrency markets," Annals of Operations Research, Springer, vol. 300(1), pages 79-96, May.
  29. Anoop C V & Neeraj Negi & Anup Aprem, 2023. "Bayesian framework for characterizing cryptocurrency market dynamics, structural dependency, and volatility using potential field," Papers 2308.01013, arXiv.org.
  30. Marco Ortu & Nicola Uras & Claudio Conversano & Giuseppe Destefanis & Silvia Bartolucci, 2021. "On Technical Trading and Social Media Indicators in Cryptocurrencies' Price Classification Through Deep Learning," Papers 2102.08189, arXiv.org, revised Feb 2021.
  31. Melisa Ozdamar & Levent Akdeniz & Ahmet Sensoy, 2021. "Lottery-like preferences and the MAX effect in the cryptocurrency market," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-27, December.
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