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

Arbeitspapier

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: IRTG 1792 Discussion Paper ; No. 2018-032

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Mathematical and Quantitative Methods: General
• Thema: Community Detection ; Dynamic Network ; Return Predictability ; Behavioural Bias ; Market Segmentation ; Bitcoin

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Guo, Li ; Tao, Yubo ; Härdle, Wolfgang Karl
• Ereignis: Veröffentlichung
• (wer): Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
• (wo): Berlin
• (wann): 2018

Weitere Informationen

• Handle: http://hdl.handle.net/10419/230743
• Letzte Aktualisierung: 10.03.2025, 11:44 MEZ