Publication Type

Working Paper

Version

publishedVersion

Publication Date

5-2018

Abstract

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 algorithms 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 confirms the economic meanings of our grouping results and reveal important portfolio management implications.

Keywords

Community Detection, Dynamic Network, Stochastic Blockmodel, Spectral Clustering, Return Predictability, Bitcoin, Behaviour Bias

Discipline

Econometrics | Finance

Research Areas

Macroeconomics

Areas of Excellence

Finance and Financial Markets

First Page

1

Last Page

47

Embargo Period

7-7-2018

Copyright Owner and License

Authors

Additional URL

https://ssrn.com/abstract=3185594

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