In a recent report from Kyoto University, graduate researchers have unveiled a fascinating connection between the price of XRP and the intricate web of transactions within its network. This research has shed light on a noteworthy anti-correlation, especially during volatile periods in the cryptocurrency market.
This report was brought to the attention of the XRP community by WrathofKahneman (@WKahneman) in a post on X. He also highlighted that the research was sponsored by the Ripple Impact Fund.
Recent study out of Kyoto University examines #XRP price during/after '18 bubble and correlations. Most interesting? (2021 is latest data) They identify a set of driver nodes during the bubble. Supported by #Ripple Impact Fund.https://t.co/K8o0lvSBNp pic.twitter.com/tdAxamRtZL
— WrathofKahneman 🪝 (@WKahneman) September 22, 2023
Understanding the Research
The heart of this study lies in the correlation tensor spectra, a mathematical tool used to decipher the structure and connections within a network. It helps uncover hidden patterns and relationships between different nodes in the network.
The research builds upon a previous study that focused on the period in 2018 commonly called the “crypto bubble.” During this time, XRP exhibited a remarkable anti-correlation with specific values derived from the correlation tensors of weekly XRP transaction networks.
Simply put, when XRP prices surged, some values extracted from transaction data declined, and when XRP prices fell, these values rose. This consistent pattern caught the researchers’ attention.
The team behind the new research expanded their methodology involving correlation tensor spectra for XRP transaction networks. They estimated and compared the distribution of large singular values. Their analysis involved a combination of random matrix theory and real-world correlation tensor data. The investigation lasted two years, and they covered bubble and non-bubble phases for XRP in the market.
The non-bubble periods showed no significant correlation between XRP’s price and the largest singular value. However, an interesting strong anti-correlation emerged during the bubble period.
Furthermore, by examining the information derived from the singular vectors, the researchers identified a set of “driver nodes” that played a pivotal role in influencing the XRP market during the bubble period.
This study has showcased the potential of correlation tensor spectra in unraveling the dynamic relationship between XRP’s price and its transaction network. It provides valuable insights into how market conditions can impact this relationship, offering a deeper understanding of the mechanics of the XRP market during both turbulent and stable periods.
Arvin Khamseh (@ArvinkNft) commended Ripple and the Ripple Impact Fund for funding the research. Another user was curious about the “nodes” referred to in the research. WrathofKahneman clarified that the nodes were not validators on the network.
In the text, the researchers defined wallets as nodes, and they can be seen as more network points. WrathofKahneman pointed out that this was a mathematical analysis.