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
Citation
GUO, Li; TAO, Yubo; and HARDLE, Wolfgang Karl.
A Dynamic Network Perspective on the Latent Group Structure of Cryptocurrencies. (2018). 1-47.
Available at: https://ink.library.smu.edu.sg/soe_research/2182
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://ssrn.com/abstract=3185594