Arbeitspapier

What makes cryptocurrencies special? Investor sentiment and return predictability during the bubble

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.

Sprache
Englisch

Erschienen in
Series: IRTG 1792 Discussion Paper ; No. 2019-016

Klassifikation
Wirtschaft
General Financial Markets: General (includes Measurement and Data)
Asset Pricing; Trading Volume; Bond Interest Rates
Thema
Cryptocurrency
Sentiment
Bubble
Return Predictability

Ereignis
Geistige Schöpfung
(wer)
Chen, Cathy Yi-Hsuan
Després, Roméo
Guo, Li
Renault, Thomas
Ereignis
Veröffentlichung
(wer)
Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
(wo)
Berlin
(wann)
2019

Handle
Letzte Aktualisierung
10.03.2025, 11:42 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Chen, Cathy Yi-Hsuan
  • Després, Roméo
  • Guo, Li
  • Renault, Thomas
  • Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"

Entstanden

  • 2019

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