IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v276y2019i2p770-780.html
   My bibliography  Save this article

Bitcoin price forecasting with neuro-fuzzy techniques

Author

Listed:
  • Atsalakis, George S.
  • Atsalaki, Ioanna G.
  • Pasiouras, Fotios
  • Zopounidis, Constantin

Abstract

Cryptocurrencies, with Bitcoin being the most notable example, have attracted considerable attention in recent years, and they have experienced large fluctuations in their price. While a few studies employ conventional statistical and econometric approaches to reveal the driving factors of Bitcoin's prices, research on the development of forecasting models to be used as decision support tools in investment strategies is scarce. This study proposes a computational intelligence technique that uses a hybrid Neuro-Fuzzy controller, namely PATSOS, to forecast the direction in the change of the daily price of Bitcoin. The proposed methodology outperforms two other computational intelligence models, the first being developed with a simpler neuro-fuzzy approach, and the second being developed with artificial neural networks. Furthermore, the investment returns achieved by a trading simulation, based on the signals of the proposed model, are 71.21% higher than the ones achieved through a naive buy-and-hold strategy. The performance of the PATSOS system is robust to the use of other cryptocurrencies.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:276:y:2019:i:2:p:770-780
    DOI: 10.1016/j.ejor.2019.01.040
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037722171930075X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2019.01.040?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. R. E. Bellman & L. A. Zadeh, 1970. "Decision-Making in a Fuzzy Environment," Management Science, INFORMS, vol. 17(4), pages 141-164, December.
    2. Yechen Zhu & David Dickinson & Jianjun Li, 2017. "Erratum to: Analysis on the influence factors of Bitcoin’s price based on VEC model," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 3(1), pages 1-1, December.
    3. Zhu, Bangzhu & Wei, Yiming, 2013. "Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology," Omega, Elsevier, vol. 41(3), pages 517-524.
    4. Tiwari, Aviral Kumar & Jana, R.K. & Das, Debojyoti & Roubaud, David, 2018. "Informational efficiency of Bitcoin—An extension," Economics Letters, Elsevier, vol. 163(C), pages 106-109.
    5. Demir, Ender & Gozgor, Giray & Lau, Chi Keung Marco & Vigne, Samuel A., 2018. "Does economic policy uncertainty predict the Bitcoin returns? An empirical investigation," Finance Research Letters, Elsevier, vol. 26(C), pages 145-149.
    6. 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.
    7. Bouri, Elie & Gupta, Rangan & Tiwari, Aviral Kumar & Roubaud, David, 2017. "Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions," Finance Research Letters, Elsevier, vol. 23(C), pages 87-95.
    8. Balcilar, Mehmet & Bouri, Elie & Gupta, Rangan & Roubaud, David, 2017. "Can volume predict Bitcoin returns and volatility? A quantiles-based approach," Economic Modelling, Elsevier, vol. 64(C), pages 74-81.
    9. Bouri, Elie & Gupta, Rangan & Lahiani, Amine & Shahbaz, Muhammad, 2018. "Testing for asymmetric nonlinear short- and long-run relationships between bitcoin, aggregate commodity and gold prices," Resources Policy, Elsevier, vol. 57(C), pages 224-235.
    10. Gandal, Neil & Hamrick, JT & Moore, Tyler & Oberman, Tali, 2018. "Price manipulation in the Bitcoin ecosystem," Journal of Monetary Economics, Elsevier, vol. 95(C), pages 86-96.
    11. Hendrickson, Joshua R. & Luther, William J., 2017. "Banning bitcoin," Journal of Economic Behavior & Organization, Elsevier, vol. 141(C), pages 188-195.
    12. Bouri, Elie & Shahzad, Syed Jawad Hussain & Roubaud, David, 2019. "Co-explosivity in the cryptocurrency market," Finance Research Letters, Elsevier, vol. 29(C), pages 178-183.
    13. Kim, Thomas, 2017. "On the transaction cost of Bitcoin," Finance Research Letters, Elsevier, vol. 23(C), pages 300-305.
    14. Cheah, Eng-Tuck & Fry, John, 2015. "Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin," Economics Letters, Elsevier, vol. 130(C), pages 32-36.
    15. Elie Bouri & Naji Jalkh & Peter Molnár & David Roubaud, 2017. "Bitcoin for energy commodities before and after the December 2013 crash: diversifier, hedge or safe haven?," Applied Economics, Taylor & Francis Journals, vol. 49(50), pages 5063-5073, October.
    16. Feng, Wenjun & Wang, Yiming & Zhang, Zhengjun, 2018. "Informed trading in the Bitcoin market," Finance Research Letters, Elsevier, vol. 26(C), pages 63-70.
    17. Yechen Zhu & David Dickinson & Jianjun Li, 2017. "Analysis on the influence factors of Bitcoin’s price based on VEC model," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 3(1), pages 1-13, December.
    18. Baur, Dirk G. & Hong, KiHoon & Lee, Adrian D., 2018. "Bitcoin: Medium of exchange or speculative assets?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 54(C), pages 177-189.
    Full references (including those not matched with items on IDEAS)

    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. Corbet, Shaen & Lucey, Brian & Urquhart, Andrew & Yarovaya, Larisa, 2019. "Cryptocurrencies as a financial asset: A systematic analysis," International Review of Financial Analysis, Elsevier, vol. 62(C), pages 182-199.
    2. 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.
    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. Gkillas, Konstantinos & Bouri, Elie & Gupta, Rangan & Roubaud, David, 2022. "Spillovers in Higher-Order Moments of Crude Oil, Gold, and Bitcoin," The Quarterly Review of Economics and Finance, Elsevier, vol. 84(C), pages 398-406.
    5. Qin, Meng & Su, Chi-Wei & Tao, Ran, 2021. "BitCoin: A new basket for eggs?," Economic Modelling, Elsevier, vol. 94(C), pages 896-907.
    6. 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.
    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. Dunbar, Kwamie & Owusu-Amoako, Johnson, 2023. "Predictability of crypto returns: The impact of trading behavior," Journal of Behavioral and Experimental Finance, Elsevier, vol. 39(C).
    9. Wei Zhang & Pengfei Wang, 2020. "Investor attention and the pricing of cryptocurrency market," Evolutionary and Institutional Economics Review, Springer, vol. 17(2), pages 445-468, July.
    10. Besma Hkiri & Juncal Cunado & Mehmet Balcilar & Rangan Gupta, 2021. "Time-varying relationship between conventional and unconventional monetary policies and risk aversion: international evidence from time- and frequency-domains," Empirical Economics, Springer, vol. 61(6), pages 2963-2983, December.
    11. Zhang, Wei & Wang, Pengfei & Li, Xiao & Shen, Dehua, 2018. "The inefficiency of cryptocurrency and its cross-correlation with Dow Jones Industrial Average," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 658-670.
    12. ORĂȘTEAN Ramona & MĂRGINEAN Silvia Cristina & SAVA Raluca, 2019. "Bitcoin In The Scientific Literature – A Bibliometric Study," Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 14(3), pages 160-174, December.
    13. 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).
    14. 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.
    15. Ángeles Cebrián-Hernández & Enrique Jiménez-Rodríguez, 2021. "Modeling of the Bitcoin Volatility through Key Financial Environment Variables: An Application of Conditional Correlation MGARCH Models," Mathematics, MDPI, vol. 9(3), pages 1-16, January.
    16. 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.
    17. Achraf Ghorbel & Wajdi Frikha & Yasmine Snene Manzli, 2022. "Testing for asymmetric non-linear short- and long-run relationships between crypto-currencies and stock markets," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 12(3), pages 387-425, September.
    18. 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.
    19. Ji, Qiang & Bouri, Elie & Gupta, Rangan & Roubaud, David, 2018. "Network causality structures among Bitcoin and other financial assets: A directed acyclic graph approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 70(C), pages 203-213.
    20. Ahmed H. Elsayed & Giray Gozgor & Chi Keung Marco Lau, 2022. "Causality and dynamic spillovers among cryptocurrencies and currency markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 2026-2040, April.

    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:eee:ejores:v:276:y:2019:i:2:p:770-780. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    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.