Original title: Time-dependent weighted directed networks of cryptocurrency interaction from high-frequency returns
Shubhangam Shukla, Mahesh Peyyala, Abhijit Chakraborty · June 2026
Cryptocurrency markets shift rapidly, making it hard to see which coins drive price changes. Researchers tracked price interactions between 2020 and 2025 using high-frequency data. They mapped connections between coins using Granger causality, a statistical test that checks if one coin's past prices help predict another's future prices. Coins that exerted more predictive pull received stronger incoming or outgoing links in the map. The researchers found that price returns showed heavy-tailed distributions, meaning sudden, extreme swings occurred often. Influence was heavily concentrated in just a few assets rather than spread evenly. Ethereum stayed the most influential cryptocurrency, while Bitcoin's influence gradually dropped. Overall, top rankings constantly changed as different coins gained and lost power, revealing a competitive, unstable market structure.
Imagine a group of friends sharing news. Usually, one friend starts a rumor, and others spread it. But the starting friend can change over time. In this paper, the researchers study how cryptocurrencies influence each other, finding that Ethereum is often the starting point, while Bitcoin's role as a starter declines.
For investors and market analysts, this reveals that cryptocurrency influence is not static. Ethereum consistently leads, while Bitcoin's importance fades. The market is highly competitive, with coins constantly rising and falling. This means investment strategies must adapt to a shifting hierarchy, not rely on past dominance.
The abstract doesn't specify limitations, but the analysis is based solely on Granger causality and price data from 2020-2025. It may not capture all forms of influence, such as external events or non-linear relationships. The findings are specific to this time period and methodology.
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Time-dependent weighted directed networks of cryptocurrency interaction from high-frequency returns — Shubhangam Shukla, Mahesh Peyyala, Abhijit Chakraborty
Abstract licence: CC0
Explanation generated by Synthesis from the abstract — it is not the original paper. This is a preprint, so it hasn't necessarily been peer reviewed.