Projecting XRP price burst by correlation tensor spectra of transaction networks

TL;DR

This paper introduces a novel correlation tensor spectral method to analyze XRP transaction networks, providing early signals for price fluctuations by examining the evolution of singular values in network data.

Contribution

The study develops a new tensor spectral analysis approach for dynamic transaction networks, linking spectral features to cryptocurrency price movements.

Findings

Largest singular value negatively correlates with XRP/USD price.

Minimum singular value coincides with price peak in January 2018.

Correlation tensor analysis reveals community structure changes.

Abstract

Cryptoassets are becoming essential in the digital economy era. XRP is one of the large market cap cryptoassets. Here, we develop a novel method of correlation tensor spectra for the dynamical XRP networks, which can provide an early indication for XRP price. A weighed directed weekly transaction network among XRP wallets is constructed by aggregating all transactions for a week. A vector for each node is then obtained by embedding the weekly network in continuous vector space. From a set of weekly snapshots of node vectors, we construct a correlation tensor. A double singular value decomposition of the correlation tensors gives its singular values. The significance of the singular values is shown by comparing with its randomize counterpart. The evolution of singular values shows a distinctive behavior. The largest singular value shows a significant negative correlation with XRP/USD price. We observe the minimum of the largest singular values at the XRP/USD price peak during the first week of January 2018. The minimum of the largest singular value during January 2018 is explained by decomposing the correlation tensor in the signal and noise components and also by evolution of community structure.