What Makes Cryptocurrencies Special? Investor Sentiment and Return Predictability

36 Pages Posted: 28 Jun 2019 Last revised: 6 Apr 2021

See all articles by Cathy Yi‐Hsuan Chen

Cathy Yi‐Hsuan Chen

University of Glasgow, Adam Smith Business School; Humboldt Universität zu Berlin

Li Guo

Fudan University - School of Economics; Shanghai Institute of International Finance and Economics

Thomas Renault

Université Paris I Panthéon-Sorbonne - Centre d'Economie de la Sorbonne (CES)

Date Written: June 3, 2019

Abstract

We propose a crypto-specific lexicon to quantify the investor sentiment and use it to predict the cryptocurrency market returns. The new lexicon achieves better accuracy (32\% higher) than the traditional financial lexicon when applied to an out-of-sample classification setting. The empirical results reveal that investor sentiment positively predicts excess CRIX returns with a daily in(out)-of-sample $R^2$ of 2.74\% (3.15\%) without significant evidence on the return reversal. The results are robust to the inclusion of alternative sentiment indices, technical indicators, market microstructure noise, and across bubble and non-bubble periods. We further exclude the soft information interpretation of our sentiment index by showing that non-fundamental related sentiment shows stronger predictive power than that of the fundamentally related sentiment. Our findings suggest that in a market-driven by noise traders, and when there is only limited information about the fundamental value of the underlying asset, investor sentiment drives the price evolution and its impact may not reverse in a short horizon.

Keywords: Cryptocurrency; Sentiment; Momentum, Return Predictability, Bitcoin

JEL Classification: G02; G10; G12

Suggested Citation

Chen, Cathy Yi‐Hsuan and Guo, Li and Renault, Thomas, What Makes Cryptocurrencies Special? Investor Sentiment and Return Predictability (June 3, 2019). Available at SSRN: https://ssrn.com/abstract=3398423 or http://dx.doi.org/10.2139/ssrn.3398423

Cathy Yi‐Hsuan Chen (Contact Author)

University of Glasgow, Adam Smith Business School ( email )

University Avenue
Glasgow, G12 8QQ
United Kingdom
01413305065 (Phone)

HOME PAGE: http://https://gla.cathychen.info

Humboldt Universität zu Berlin ( email )

Unter den Linden 6,
Berlin, 10117
Germany
03020935631 (Phone)
10099 (Fax)

Li Guo

Fudan University - School of Economics ( email )

600 GuoQuan Road
Shanghai, 200433
China

Shanghai Institute of International Finance and Economics ( email )

777 Guoding Rd
Wu Jiao Chang, Yangpu District
Shanghai, Shanghai 200017
China

Thomas Renault

Université Paris I Panthéon-Sorbonne - Centre d'Economie de la Sorbonne (CES) ( email )

106-112 Boulevard de l'hopital
106-112 Boulevard de l'Hôpital
Paris Cedex 13, 75647
France

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
281
Abstract Views
1,398
rank
129,754
PlumX Metrics