Understanding Latent Group Structure of Cryptocurrencies Market: A Dynamic Network Perspective

In this paper, we study the latent group structure in cryptocurrencies market by forming a dynamic return inferred network with coin attributions. We develop a dynamic covariate-assisted spectral clustering method to detect the communities in dynamic network framework and prove its uniform consistency along the horizons. Applying our new method, we show the return inferred network structure and coin attributions, including algorithm and proof types, jointly determine the market segmentation. Based on the network model, we propose a novel "hard-to-value" measure using the centrality scores. Further analysis reveals that the group with a lower centrality score exhibits stronger short-term return reversals. Cross-sectional return predictability further conrms the economic meanings of our grouping results and reveal important portfolio management implications.