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Predicting the price of Bitcoin by the most frequent edges of its transaction network

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  • Kurbucz, Marcell Tamás

Abstract

Research on the Bitcoin transaction network has increased rapidly in recent years, but still, little is known about the network’s influence on Bitcoin prices. The goals of this paper are twofold: to determine the predictive power of the transaction network’s most frequent edges on the future price of Bitcoin and to provide an efficient technique for applying this untapped dataset in day trading. To accomplish these goals, a complex method consisting of single-hidden layer feedforward neural networks (SLFNs) is used. Based on the results, the presented method achieved an accuracy of approximately 60.05% during daily price movement classifications, despite only considering a small subset of edges.

Suggested Citation

  • Kurbucz, Marcell Tamás, 2019. "Predicting the price of Bitcoin by the most frequent edges of its transaction network," Economics Letters, Elsevier, vol. 184(C).
    • Handle: RePEc:eee:ecolet:v:184:y:2019:i:c:s0165176519303271
    DOI: 10.1016/j.econlet.2019.108655
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    References listed on IDEAS

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    1. 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.
    2. Katsiampa, Paraskevi, 2017. "Volatility estimation for Bitcoin: A comparison of GARCH models," Economics Letters, Elsevier, vol. 158(C), pages 3-6.
    3. Dyhrberg, Anne Haubo, 2016. "Bitcoin, gold and the dollar – A GARCH volatility analysis," Finance Research Letters, Elsevier, vol. 16(C), pages 85-92.
    4. Urquhart, Andrew, 2016. "The inefficiency of Bitcoin," Economics Letters, Elsevier, vol. 148(C), pages 80-82.
    5. Akcora, Cuneyt Gurcan & Dixon, Matthew F. & Gel, Yulia R. & Kantarcioglu, Murat, 2018. "Bitcoin risk modeling with blockchain graphs," Economics Letters, Elsevier, vol. 173(C), pages 138-142.
    6. Cuneyt Akcora & Matthew Dixon & Yulia Gel & Murat Kantarcioglu, 2018. "Bitcoin Risk Modeling with Blockchain Graphs," Papers 1805.04698, arXiv.org.
    7. Koutmos, Dimitrios, 2018. "Bitcoin returns and transaction activity," Economics Letters, Elsevier, vol. 167(C), pages 81-85.
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    Citations

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    Cited by:

    1. Yuanyuan (Catherine) Chen, 2021. "Empirical analysis of bitcoin price," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 45(4), pages 692-715, October.
    2. 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).
    3. Marthinsen, John E. & Gordon, Steven R., 2022. "The price and cost of bitcoin," The Quarterly Review of Economics and Finance, Elsevier, vol. 85(C), pages 280-288.
    4. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & David Martinez-Rego & Fan Wu & Lingbo Li, 2022. "Cryptocurrency trading: a comprehensive survey," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-59, December.
    5. John E. Marthinsen & Steven R. Gordon, 2022. "The Price and Cost of Bitcoin," Papers 2204.13102, arXiv.org.
    6. 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.
    7. Ji, Qiang & Bouri, Elie & Kristoufek, Ladislav & Lucey, Brian, 2021. "Realised volatility connectedness among Bitcoin exchange markets," Finance Research Letters, Elsevier, vol. 38(C).
    8. Dag, Ali & Dag, Asli Z. & Asilkalkan, Abdullah & Simsek, Serhat & Delen, Dursun, 2023. "A Tree Augmented Naïve Bayes-based methodology for classifying cryptocurrency trends," Journal of Business Research, Elsevier, vol. 156(C).
    9. Rico-Peña, Juan Jesús & Arguedas-Sanz, Raquel & López-Martin, Carmen, 2023. "Models used to characterise blockchain features. A systematic literature review and bibliometric analysis," Technovation, Elsevier, vol. 123(C).
    10. Yufang Wang & Haiyan Wang, 2020. "Using Networks and Partial Differential Equations to Predict Bitcoin Price," Papers 2001.03099, arXiv.org.
    11. Sasan Barak & Navid Parvini, 2023. "Transfer‐entropy‐based dynamic feature selection for evaluating Bitcoin price drivers," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(12), pages 1695-1726, December.

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    More about this item

    Keywords

    Bitcoin; Transaction network; Price prediction; Artificial neural network;
    All these keywords.

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General

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