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Segmenting Bitcoin Transactions for Price Movement Prediction

Author

Listed:
  • Yuxin Zhang

    (Technology, Information Systems and Analytics, Mike Ilitch School of Business, Wayne State University, Detroit, MI 48201, USA)

  • Rajiv Garg

    (Information Systems and Operations Management, Goizueta Business School, Emory University, Atlanta, GA 30322, USA)

  • Linda L. Golden

    (Department of Marketing, McCombs School of Business, The University of Texas at Austin, Austin, TX 78712, USA)

  • Patrick L. Brockett

    (Department of Information, Risk and Operations Management, McCombs School of Business, The University of Texas at Austin, Austin, TX 78712, USA)

  • Ajit Sharma

    (Technology, Information Systems and Analytics, Mike Ilitch School of Business, Wayne State University, Detroit, MI 48201, USA)

Abstract

Cryptocurrencies like Bitcoin have received substantial attention from financial exchanges. Unfortunately, arbitrage-based financial market price prediction models are ineffective for cryptocurrencies. In this paper, we utilize standard machine learning models and publicly available transaction data in blocks to predict the direction of Bitcoin price movement. We illustrate our methodology using data we merged from the Bitcoin blockchain and various online sources. This gave us the Bitcoin transaction history (block IDs, block timestamps, transaction IDs, senders’ addresses, receivers’ addresses, transaction amounts), as well as the market exchange price, for the period from 13 September 2011 to 5 May 2017. We show that segmenting publicly available transactions based on investor typology helps achieve higher prediction accuracy compared to the existing Bitcoin price movement prediction models in the literature. This transaction segmentation highlights the role of investor types in impacting financial markets. Managerially, the segmentation of financial transactions helps us understand the role of financial and cryptocurrency market participants in asset price movements. These findings provide further implications for risk management, financial regulation, and investment strategies in this new era of digital currencies.

Suggested Citation

  • Yuxin Zhang & Rajiv Garg & Linda L. Golden & Patrick L. Brockett & Ajit Sharma, 2024. "Segmenting Bitcoin Transactions for Price Movement Prediction," JRFM, MDPI, vol. 17(3), pages 1-17, March.
    • Handle: RePEc:gam:jjrfmx:v:17:y:2024:i:3:p:128-:d:1361179
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    References listed on IDEAS

    as
    1. Patrick L. Brockett & Linda L. Golden & Jaeho Jang & Chuanhou Yang, 2006. "A Comparison of Neural Network, Statistical Methods, and Variable Choice for Life Insurers' Financial Distress Prediction," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 73(3), pages 397-419, September.
    2. Tobias Adrian & Tommaso Mancini-Griffoli, 2021. "The Rise of Digital Money," Annual Review of Financial Economics, Annual Reviews, vol. 13(1), pages 57-77, November.
    3. Richard Holden & Anup Malani, 2022. "An Examination of Velocity and Initial Coin Offerings," Management Science, INFORMS, vol. 68(12), pages 9026-9041, December.
    4. Jang, Jeewon & Kang, Jangkoo, 2019. "Probability of price crashes, rational speculative bubbles, and the cross-section of stock returns," Journal of Financial Economics, Elsevier, vol. 132(1), pages 222-247.
    5. Aalborg, Halvor Aarhus & Molnár, Peter & de Vries, Jon Erik, 2019. "What can explain the price, volatility and trading volume of Bitcoin?," Finance Research Letters, Elsevier, vol. 29(C), pages 255-265.
    6. Frode Kjærland & Aras Khazal & Erlend A. Krogstad & Frans B. G. Nordstrøm & Are Oust, 2018. "An Analysis of Bitcoin’s Price Dynamics," JRFM, MDPI, vol. 11(4), pages 1-18, October.
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