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Cross Cryptocurrency Relationship Mining for Bitcoin Price Prediction

Author

Listed:
  • Panpan Li
  • Shengbo Gong
  • Shaocong Xu
  • Jiajun Zhou
  • Yu Shanqing
  • Qi Xuan

Abstract

Blockchain finance has become a part of the world financial system, most typically manifested in the attention to the price of Bitcoin. However, a great deal of work is still limited to using technical indicators to capture Bitcoin price fluctuation, with little consideration of historical relationships and interactions between related cryptocurrencies. In this work, we propose a generic Cross-Cryptocurrency Relationship Mining module, named C2RM, which can effectively capture the synchronous and asynchronous impact factors between Bitcoin and related Altcoins. Specifically, we utilize the Dynamic Time Warping algorithm to extract the lead-lag relationship, yielding Lead-lag Variance Kernel, which will be used for aggregating the information of Altcoins to form relational impact factors. Comprehensive experimental results demonstrate that our C2RM can help existing price prediction methods achieve significant performance improvement, suggesting the effectiveness of Cross-Cryptocurrency interactions on benefitting Bitcoin price prediction.

Suggested Citation

  • Panpan Li & Shengbo Gong & Shaocong Xu & Jiajun Zhou & Yu Shanqing & Qi Xuan, 2022. "Cross Cryptocurrency Relationship Mining for Bitcoin Price Prediction," Papers 2205.00974, arXiv.org.
    • Handle: RePEc:arx:papers:2205.00974
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    References listed on IDEAS

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    1. Nazmiye Ceren Abay & Cuneyt Gurcan Akcora & Yulia R. Gel & Umar D. Islambekov & Murat Kantarcioglu & Yahui Tian & Bhavani Thuraisingham, 2019. "ChainNet: Learning on Blockchain Graphs with Topological Features," Papers 1908.06971, arXiv.org.
    2. 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.
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