Cross Cryptocurrency Relationship Mining for Bitcoin Price Prediction

TL;DR

This paper introduces C2RM, a module that captures relationships between Bitcoin and related cryptocurrencies, improving price prediction accuracy by leveraging cross-cryptocurrency interactions.

Contribution

The paper presents a novel C2RM module that effectively models synchronous and asynchronous impacts between Bitcoin and Altcoins for enhanced price prediction.

Findings

C2RM significantly improves Bitcoin price prediction performance.

Dynamic Time Warping effectively captures lead-lag relationships.

Cross-cryptocurrency interactions are beneficial for price forecasting.

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.