Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: http://hdl.handle.net/10419/230792
Erscheinungsjahr: 
2019
Schriftenreihe/Nr.: 
IRTG 1792 Discussion Paper No. 2019-016
Verlag: 
Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series", Berlin
Zusammenfassung: 
The 2017 bubble on the cryptocurrency market recalls our memory in the dot-com bubble, during which hard-to-measure fundamentals and investors’ illusion for brand new technologies led to overvalued prices. Benefiting from the massive increase in the volume of messages published on social media and message boards, we examine the impact of investor sentiment, conditional on bubble regimes, on cryptocurrencies aggregate return prediction. Constructing a crypto-specific lexicon and using a local-momentum autoregression model, we find that the sentiment effect is prolonged and sustained during the bubble while it turns out a reversal effect once the bubble collapsed. The out-of-sample analysis along with portfolio analysis is conducted in this study. When measuring investor sentiment for a new type of asset such as cryptocurrencies, we highlight that the impact of investor sentiment on cryptocurrency returns is conditional on bubble regimes.
Schlagwörter: 
Cryptocurrency
Sentiment
Bubble
Return Predictability
JEL: 
G02
G10
G12
Dokumentart: 
Working Paper

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