  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.
