Cross Cryptocurrency Relationship Mining for Bitcoin Price Prediction
This addresses the problem of improving Bitcoin price prediction accuracy for financial analysts by incorporating cross-cryptocurrency interactions, though it is incremental as it builds on existing prediction methods.
The paper tackles Bitcoin price prediction by proposing a Cross-Cryptocurrency Relationship Mining (C2RM) module to capture synchronous and asynchronous impacts from related altcoins, using Dynamic Time Warping to extract lead-lag relationships, and shows it helps existing methods achieve significant performance improvement.
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.