A Time-Varying Network for Cryptocurrencies

TL;DR

This paper constructs a dynamic cryptocurrency network based on return predictability and technological similarity, introducing a novel clustering method to identify communities and improve risk diversification strategies.

Contribution

It develops a dynamic covariate-assisted spectral clustering method for estimating cryptocurrency communities considering multiple factors, enhancing understanding of market segmentation.

Findings

Investors can diversify risk by investing across different communities.

A momentum trading strategy yields a 1.08% daily return.

Results are not driven by behavioral biases.

Abstract

Cryptocurrencies return cross-predictability and technological similarity yield information on risk propagation and market segmentation. To investigate these effects, we build a time-varying network for cryptocurrencies, based on the evolution of return cross-predictability and technological similarities. We develop a dynamic covariate-assisted spectral clustering method to consistently estimate the latent community structure of cryptocurrencies network that accounts for both sets of information. We demonstrate that investors can achieve better risk diversification by investing in cryptocurrencies from different communities. A cross-sectional portfolio that implements an inter-crypto momentum trading strategy earns a 1.08% daily return. By dissecting the portfolio returns on behavioral factors, we confirm that our results are not driven by behavioral mechanisms.