Analyzing the world of cryptoassets, particularly focusing on XRP networks, has become essential in understanding the digital economy. This study delves into correlation tensor spectra to provide insights into the dynamics of XRP transactions and their correlation with XRP/USD price movements.
The research method involves constructing a correlation tensor from XRP transaction networks, utilizing DeepWalk embedding techniques to map the networks into vector space. By performing a double singular value decomposition on the correlation tensors, the study uncovers correlations between network structures and XRP prices.
The evolution of the largest singular value in the correlation tensors shows distinctive behavior, particularly during bubble periods in XRP prices. By decomposing the correlation tensor into signal and noise components, the study explains the significance of the largest singular value in predicting XRP/USD price trends.
Furthermore, the study explores the relationship between community structures within XRP networks and the correlation tensor, highlighting how disruptions in community formations can impact the correlations and predictions related to XRP prices.
Overall, this research demonstrates the potential of correlation tensor analysis in providing early indications for price movements in the cryptoasset market, specifically focusing on XRP networks. Stay informed for more updates on the evolving dynamics of cryptoassets and financial markets.
Attribution:
This article was summarized and republished from the original source.
Please check the original article here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10033910/.