IDEAS home Printed from https://ideas.repec.org/a/gam/jforec/v3y2021i2p24-420d564101.html
   My bibliography  Save this article

Is It Possible to Forecast the Price of Bitcoin?

Author

Listed:
  • Julien Chevallier

    (IPAG Lab, IPAG Business School, 184 Boulevard Saint-Germain, 75006 Paris, France
    Economics Department, Université Paris 8 (LED), 2 rue de la Liberté, 93526 Saint-Denis, France
    These authors contributed equally to this work.)

  • Dominique Guégan

    (Applied Mathematics Department, Université Paris 1 Panthéon-Sorbonne, LabEx ReFi, 106 Boulevard de l’Hopital, CEDEX 13, 75647 Paris, France
    Department of Economics, University Ca’Foscari of Venezia, 30123 Venice, Italy
    These authors contributed equally to this work.)

  • Stéphane Goutte

    (CEMOTEV, UVSQ, Paris-Saclay, 78280 Guyancourt, France
    International School, Vietnam National University, Hanoi 10000, Vietnam)

Abstract

This paper focuses on forecasting the price of Bitcoin, motivated by its market growth and the recent interest of market participants and academics. We deploy six machine learning algorithms (e.g., Artificial Neural Network, Support Vector Machine, Random Forest, k -Nearest Neighbours, AdaBoost, Ridge regression), without deciding a priori which one is the ‘best’ model. The main contribution is to use these data analytics techniques with great caution in the parameterization, instead of classical parametric modelings (AR), to disentangle the non-stationary behavior of the data. As soon as Bitcoin is also used for diversification in portfolios, we need to investigate its interactions with stocks, bonds, foreign exchange, and commodities. We identify that other cryptocurrencies convey enough information to explain the daily variation of Bitcoin’s spot and futures prices. Forecasting results point to the segmentation of Bitcoin concerning alternative assets. Finally, trading strategies are implemented.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jforec:v:3:y:2021:i:2:p:24-420:d:564101
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-9394/3/2/24/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2571-9394/3/2/24/
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Le, TN-Lan & Abakah, Emmanuel Joel Aikins & Tiwari, Aviral Kumar, 2021. "Time and frequency domain connectedness and spill-over among fintech, green bonds and cryptocurrencies in the age of the fourth industrial revolution," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    2. Dimitrios Koutmos, 2020. "Market risk and Bitcoin returns," Annals of Operations Research, Springer, vol. 294(1), pages 453-477, November.
    3. Atsalakis, George S. & Atsalaki, Ioanna G. & Pasiouras, Fotios & Zopounidis, Constantin, 2019. "Bitcoin price forecasting with neuro-fuzzy techniques," European Journal of Operational Research, Elsevier, vol. 276(2), pages 770-780.
    4. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2018. "Bitcoin technical trading with artificial neural network," CIRJE F-Series CIRJE-F-1078, CIRJE, Faculty of Economics, University of Tokyo.
    5. Julien Prat & Benjamin Walter, 2021. "An Equilibrium Model of the Market for Bitcoin Mining," Journal of Political Economy, University of Chicago Press, vol. 129(8), pages 2415-2452.
    6. Dominique Guegan & Marius Cristian Frunza, 2018. "Is the Bitcoin Rush Over?," Post-Print halshs-01822992, HAL.
    7. Fang, Libing & Bouri, Elie & Gupta, Rangan & Roubaud, David, 2019. "Does global economic uncertainty matter for the volatility and hedging effectiveness of Bitcoin?," International Review of Financial Analysis, Elsevier, vol. 61(C), pages 29-36.
    8. Sun, Xiaolei & Liu, Mingxi & Sima, Zeqian, 2020. "A novel cryptocurrency price trend forecasting model based on LightGBM," Finance Research Letters, Elsevier, vol. 32(C).
    9. Max H. Farrell & Tengyuan Liang & Sanjog Misra, 2021. "Deep Neural Networks for Estimation and Inference," Econometrica, Econometric Society, vol. 89(1), pages 181-213, January.
    10. Marie Briere & Kim Oosterlinck & Ariane Szafarz, 2015. "Virtual Currency, Tangible Return: Portfolio Diversification with Bitcoins," Post-Print CEB, ULB -- Universite Libre de Bruxelles, vol. 16(6), pages 365-373.
    11. Billio, Monica & Getmansky, Mila & Lo, Andrew W. & Pelizzon, Loriana, 2012. "Econometric measures of connectedness and systemic risk in the finance and insurance sectors," Journal of Financial Economics, Elsevier, vol. 104(3), pages 535-559.
    12. Baumöhl, Eduard, 2019. "Are cryptocurrencies connected to forex? A quantile cross-spectral approach," Finance Research Letters, Elsevier, vol. 29(C), pages 363-372.
    13. John M. Griffin & Amin Shams, 2020. "Is Bitcoin Really Untethered?," Journal of Finance, American Finance Association, vol. 75(4), pages 1913-1964, August.
    14. Khuntia, Sashikanta & Pattanayak, J.K., 2020. "Adaptive long memory in volatility of intra-day bitcoin returns and the impact of trading volume," Finance Research Letters, Elsevier, vol. 32(C).
    15. Alvarez-Ramirez, J. & Rodriguez, E. & Ibarra-Valdez, C., 2018. "Long-range correlations and asymmetry in the Bitcoin market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 948-955.
    16. Dominique Guégan & Marius Cristian Frunza, 2018. "Is the Bitcoin Rush Over?," Documents de travail du Centre d'Economie de la Sorbonne 18014, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    17. Kapar, Burcu & Olmo, Jose, 2019. "An analysis of price discovery between Bitcoin futures and spot markets," Economics Letters, Elsevier, vol. 174(C), pages 62-64.
    18. Selmi, Refk & Mensi, Walid & Hammoudeh, Shawkat & Bouoiyour, Jamal, 2018. "Is Bitcoin a hedge, a safe haven or a diversifier for oil price movements? A comparison with gold," Energy Economics, Elsevier, vol. 74(C), pages 787-801.
    19. Robert Hudson & Andrew Urquhart, 2021. "Technical trading and cryptocurrencies," Annals of Operations Research, Springer, vol. 297(1), pages 191-220, February.
    20. Niklas Bussmann & Paolo Giudici & Dimitri Marinelli & Jochen Papenbrock, 2021. "Explainable Machine Learning in Credit Risk Management," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 203-216, January.
    21. Fantazzini, Dean & Nigmatullin, Erik & Sukhanovskaya, Vera & Ivliev, Sergey, 2016. "Everything you always wanted to know about bitcoin modelling but were afraid to ask. I," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 44, pages 5-24.
    22. Robert Tibshirani, 2011. "Regression shrinkage and selection via the lasso: a retrospective," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(3), pages 273-282, June.
    23. Ting-Hsuan Chen & Mu-Yen Chen & Guan-Ting Du, 2021. "The Determinants of Bitcoin’s Price: Utilization of GARCH and Machine Learning Approaches," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 267-280, January.
    24. Guégan, Dominique & Renault, Thomas, 2021. "Does investor sentiment on social media provide robust information for Bitcoin returns predictability?," Finance Research Letters, Elsevier, vol. 38(C).
    25. Gillaizeau, Marc & Jayasekera, Ranadeva & Maaitah, Ahmad & Mishra, Tapas & Parhi, Mamata & Volokitina, Evgeniia, 2019. "Giver and the receiver: Understanding spillover effects and predictive power in cross-market Bitcoin prices," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 86-104.
    26. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    27. Louis Abraham & Dominique Guegan, 2019. "The other side of the Coin: Risks of the Libra Blockchain," Post-Print halshs-02325808, HAL.
    28. Abedifar, Pejman & Giudici, Paolo & Hashem, Shatha Qamhieh, 2017. "Heterogeneous market structure and systemic risk: Evidence from dual banking systems," Journal of Financial Stability, Elsevier, vol. 33(C), pages 96-119.
    29. Shen, Dehua & Urquhart, Andrew & Wang, Pengfei, 2019. "Does twitter predict Bitcoin?," Economics Letters, Elsevier, vol. 174(C), pages 118-122.
    30. Alberto Abadie & Maximilian Kasy, 2019. "Choosing Among Regularized Estimators in Empirical Economics: The Risk of Machine Learning," The Review of Economics and Statistics, MIT Press, vol. 101(5), pages 743-762, December.
    31. Makarov, Igor & Schoar, Antoinette, 2020. "Trading and arbitrage in cryptocurrency markets," LSE Research Online Documents on Economics 100409, London School of Economics and Political Science, LSE Library.
    32. Caporale, Guglielmo Maria & Zekokh, Timur, 2019. "Modelling volatility of cryptocurrencies using Markov-Switching GARCH models," Research in International Business and Finance, Elsevier, vol. 48(C), pages 143-155.
    33. Klein, Tony & Pham Thu, Hien & Walther, Thomas, 2018. "Bitcoin is not the New Gold – A comparison of volatility, correlation, and portfolio performance," International Review of Financial Analysis, Elsevier, vol. 59(C), pages 105-116.
    34. Zargar, Faisal Nazir & Kumar, Dilip, 2019. "Informational inefficiency of Bitcoin: A study based on high-frequency data," Research in International Business and Finance, Elsevier, vol. 47(C), pages 344-353.
    35. Kurka, Josef, 2019. "Do cryptocurrencies and traditional asset classes influence each other?," Finance Research Letters, Elsevier, vol. 31(C), pages 38-46.
    36. Aslanidis, Nektarios & Bariviera, Aurelio F. & Martínez-Ibañez, Oscar, 2019. "An analysis of cryptocurrencies conditional cross correlations," Finance Research Letters, Elsevier, vol. 31(C), pages 130-137.
    37. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    38. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2018. "Bitcoin technical trading with artificial neural network," CARF F-Series CARF-F-430, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    39. Erdinc Akyildirim & Ahmet Goncu & Ahmet Sensoy, 2021. "Prediction of cryptocurrency returns using machine learning," Annals of Operations Research, Springer, vol. 297(1), pages 3-36, February.
    40. Sean Foley & Jonathan R Karlsen & Tālis J Putniņš, 2019. "Sex, Drugs, and Bitcoin: How Much Illegal Activity Is Financed through Cryptocurrencies?," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 1798-1853.
    41. Camila Epprecht & Dominique Guegan & Álvaro Veiga, 2013. "Comparing variable selection techniques for linear regression: LASSO and Autometrics," Documents de travail du Centre d'Economie de la Sorbonne 13080, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    42. Phillip, Andrew & Chan, Jennifer & Peiris, Shelton, 2019. "On long memory effects in the volatility measure of Cryptocurrencies," Finance Research Letters, Elsevier, vol. 28(C), pages 95-100.
    43. Bouri, Elie & Molnár, Peter & Azzi, Georges & Roubaud, David & Hagfors, Lars Ivar, 2017. "On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier?," Finance Research Letters, Elsevier, vol. 20(C), pages 192-198.
    44. Guesmi, Khaled & Saadi, Samir & Abid, Ilyes & Ftiti, Zied, 2019. "Portfolio diversification with virtual currency: Evidence from bitcoin," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 431-437.
    45. Stephanie F. Cheng & Gus De Franco & Haibo Jiang & Pengkai Lin, 2019. "Riding the Blockchain Mania: Public Firms’ Speculative 8-K Disclosures," Management Science, INFORMS, vol. 65(12), pages 5901-5913, December.
    46. Paolo Giudici & Gloria Polinesi, 2021. "Crypto price discovery through correlation networks," Annals of Operations Research, Springer, vol. 299(1), pages 443-457, April.
    47. Philippas, Dionisis & Rjiba, Hatem & Guesmi, Khaled & Goutte, Stéphane, 2019. "Media attention and Bitcoin prices," Finance Research Letters, Elsevier, vol. 30(C), pages 37-43.
    48. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    49. Mensi, Walid & Sensoy, Ahmet & Aslan, Aylin & Kang, Sang Hoon, 2019. "High-frequency asymmetric volatility connectedness between Bitcoin and major precious metals markets," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    50. Stjepan Beguv{s}i'c & Zvonko Kostanjv{c}ar & H. Eugene Stanley & Boris Podobnik, 2018. "Scaling properties of extreme price fluctuations in Bitcoin markets," Papers 1803.08405, arXiv.org.
    51. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2018. "Bitcoin technical trading with artificial neural network," CARF F-Series CARF-F-441, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    52. Dirk G. Baur & Thomas Dimpfl, 2019. "Price discovery in bitcoin spot or futures?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(7), pages 803-817, July.
    53. Zhao, Meng & Kulasekera, K.B., 2006. "Consistent linear model selection," Statistics & Probability Letters, Elsevier, vol. 76(5), pages 520-530, March.
    54. Makarov, Igor & Schoar, Antoinette, 2020. "Trading and arbitrage in cryptocurrency markets," Journal of Financial Economics, Elsevier, vol. 135(2), pages 293-319.
    55. Bariviera, Aurelio F. & Basgall, María José & Hasperué, Waldo & Naiouf, Marcelo, 2017. "Some stylized facts of the Bitcoin market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 484(C), pages 82-90.
    56. Carol Alexander & Jaehyuk Choi & Heungju Park & Sungbin Sohn, 2020. "BitMEX bitcoin derivatives: Price discovery, informational efficiency, and hedging effectiveness," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(1), pages 23-43, January.
    57. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2018. "Bitcoin Technical Trading with Articial Neural Network," CIRJE F-Series CIRJE-F-1090, CIRJE, Faculty of Economics, University of Tokyo.
    58. Daniel Felix Ahelegbey & Monica Billio & Roberto Casarin, 2016. "Bayesian Graphical Models for STructural Vector Autoregressive Processes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(2), pages 357-386, March.
    59. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    60. Yanhao (Max) Wei & Anthony Dukes, 2021. "Cryptocurrency Adoption with Speculative Price Bubbles," Marketing Science, INFORMS, vol. 40(2), pages 241-260, March.
    61. Nicola, Giancarlo & Cerchiello, Paola & Aste, Tomaso, 2020. "Information network modeling for U.S. banking systemic risk," LSE Research Online Documents on Economics 107563, London School of Economics and Political Science, LSE Library.
    62. Dominique Guegan, 2019. "Risks and Blockchain," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02129864, HAL.
    63. Branka Hadji Misheva & Joerg Osterrieder & Ali Hirsa & Onkar Kulkarni & Stephen Fung Lin, 2021. "Explainable AI in Credit Risk Management," Papers 2103.00949, arXiv.org.
    64. Geuder, Julian & Kinateder, Harald & Wagner, Niklas F., 2019. "Cryptocurrencies as financial bubbles: The case of Bitcoin," Finance Research Letters, Elsevier, vol. 31(C).
    65. Easley, David & O'Hara, Maureen & Basu, Soumya, 2019. "From mining to markets: The evolution of bitcoin transaction fees," Journal of Financial Economics, Elsevier, vol. 134(1), pages 91-109.
    66. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
    67. Jonathan Iworiso & Spyridon Vrontos, 2020. "On the directional predictability of equity premium using machine learning techniques," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 449-469, April.
    68. Louis Abraham & Dominique Guégan, 2019. "The other side of the Coin: Risks of the Libra Blockchain," Documents de travail du Centre d'Economie de la Sorbonne 19015, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    69. Mensi, Walid & Al-Yahyaee, Khamis Hamed & Kang, Sang Hoon, 2019. "Structural breaks and double long memory of cryptocurrency prices: A comparative analysis from Bitcoin and Ethereum," Finance Research Letters, Elsevier, vol. 29(C), pages 222-230.
    70. Marius Cristian Frunza & Dominique Guégan, 2018. "Is the Bitcoin Rush Over?," Working Papers 2018:10, Department of Economics, University of Venice "Ca' Foscari".
    71. Ahmed Mohamed Dahir & Fauziah Mahat & Bany-Ariffin Amin Noordin & Nazrul Hisyam Ab Razak, 2020. "Dynamic connectedness between Bitcoin and equity market information across BRICS countries," International Journal of Managerial Finance, Emerald Group Publishing Limited, vol. 16(3), pages 357-371, January.
    72. Dominique Guegan & Marius Cristian Frunza, 2018. "Is the Bitcoin Rush Over?," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01822992, HAL.
    73. Begušić, Stjepan & Kostanjčar, Zvonko & Eugene Stanley, H. & Podobnik, Boris, 2018. "Scaling properties of extreme price fluctuations in Bitcoin markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 400-406.
    74. Oliver Entrop & Bart Frijns & Marco Seruset, 2020. "The determinants of price discovery on bitcoin markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(5), pages 816-837, May.
    75. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    76. Dyhrberg, Anne Haubo, 2016. "Hedging capabilities of bitcoin. Is it the virtual gold?," Finance Research Letters, Elsevier, vol. 16(C), pages 139-144.
    77. Louis Abraham & Dominique Guégan, 2019. "The other side of the Coin: Risks of the Libra Blockchain," Working Papers 2019: 30, Department of Economics, University of Venice "Ca' Foscari".
    78. Louis Abraham & Dominique Guegan, 2019. "The other side of the Coin: Risks of the Libra Blockchain," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02325808, HAL.
    79. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    80. Dominique Guegan, 2019. "Risks and Blockchain," Post-Print halshs-02129864, HAL.
    81. Qingyuan Zhao & Trevor Hastie, 2021. "Causal Interpretations of Black-Box Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 272-281, January.
    82. Nakano, Masafumi & Takahashi, Akihiko & Takahashi, Soichiro, 2018. "Bitcoin technical trading with artificial neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 587-609.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zeyd Boukhers & Azeddine Bouabdallah & Cong Yang & Jan Jurjens, 2022. "Beyond Trading Data: The Hidden Influence of Public Awareness and Interest on Cryptocurrency Volatility," Papers 2202.08967, arXiv.org, revised Oct 2024.
    2. Kate Murray & Andrea Rossi & Diego Carraro & Andrea Visentin, 2023. "On Forecasting Cryptocurrency Prices: A Comparison of Machine Learning, Deep Learning, and Ensembles," Forecasting, MDPI, vol. 5(1), pages 1-14, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Helder Miguel Correia Virtuoso Sebastião & Paulo José Osório Rupino Da Cunha & Pedro Manuel Cortesão Godinho, 2021. "Cryptocurrencies and blockchain. Overview and future perspectives," International Journal of Economics and Business Research, Inderscience Enterprises Ltd, vol. 21(3), pages 305-342.
    2. Flori, Andrea, 2019. "News and subjective beliefs: A Bayesian approach to Bitcoin investments," Research in International Business and Finance, Elsevier, vol. 50(C), pages 336-356.
    3. Ahmed, Walid M.A., 2022. "Robust drivers of Bitcoin price movements: An extreme bounds analysis," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    4. Cynthia Weiyi Cai & Rui Xue & Bi Zhou, 2023. "Cryptocurrency puzzles: a comprehensive review and re-introduction," Journal of Accounting Literature, Emerald Group Publishing Limited, vol. 46(1), pages 26-50, June.
    5. Aurelio F. Bariviera & Ignasi Merediz‐Solà, 2021. "Where Do We Stand In Cryptocurrencies Economic Research? A Survey Based On Hybrid Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 377-407, April.
    6. 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).
    7. Andrea Flori, 2019. "Cryptocurrencies In Finance: Review And Applications," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(05), pages 1-22, August.
    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. Xinyu Huang & Weihao Han & David Newton & Emmanouil Platanakis & Dimitrios Stafylas & Charles Sutcliffe, 2023. "The diversification benefits of cryptocurrency asset categories and estimation risk: pre and post Covid-19," The European Journal of Finance, Taylor & Francis Journals, vol. 29(7), pages 800-825, May.
    10. Zhang, Dingxuan & Sun, Yuying & Duan, Hongbo & Hong, Yongmiao & Wang, Shouyang, 2023. "Speculation or currency? Multi-scale analysis of cryptocurrencies—The case of Bitcoin," International Review of Financial Analysis, Elsevier, vol. 88(C).
    11. 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.
    12. Bedi, Prateek & Nashier, Tripti, 2020. "On the investment credentials of Bitcoin: A cross-currency perspective," Research in International Business and Finance, Elsevier, vol. 51(C).
    13. Parthajit Kayal & Purnima Rohilla, 2021. "Bitcoin in the economics and finance literature: a survey," SN Business & Economics, Springer, vol. 1(7), pages 1-21, July.
    14. Helder Sebastião & Pedro Godinho, 2021. "Forecasting and trading cryptocurrencies with machine learning under changing market conditions," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-30, December.
    15. Chu, Jeffrey & Chan, Stephen & Zhang, Yuanyuan, 2021. "Bitcoin versus high-performance technology stocks in diversifying against global stock market indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).
    16. Shimeng Shi & Jia Zhai & Yingying Wu, 2024. "Informational inefficiency on bitcoin futures," The European Journal of Finance, Taylor & Francis Journals, vol. 30(6), pages 642-667, April.
    17. Gradojevic, Nikola & Kukolj, Dragan & Adcock, Robert & Djakovic, Vladimir, 2023. "Forecasting Bitcoin with technical analysis: A not-so-random forest?," International Journal of Forecasting, Elsevier, vol. 39(1), pages 1-17.
    18. 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).
    19. 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).
    20. Saketh Aleti & Bruce Mizrach, 2021. "Bitcoin spot and futures market microstructure," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(2), pages 194-225, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jforec:v:3:y:2021:i:2:p:24-420:d:564101. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.