Wikipedia and Digital Currencies: Interplay Between Collective Attention and Market Performance

30 Pages Posted: 1 Apr 2019

See all articles by Abeer ElBahrawy

Abeer ElBahrawy

City University London, School of Engineering and Mathematical Sciences; The Alan Turing Institute

Laura Alessandretti

City University London, School of Engineering and Mathematical Sciences

Andrea Baronchelli

City University London - School of Engineering and Mathematical Sciences; City University London

Date Written: March 4, 2019

Abstract

The production and consumption of information about Bitcoin and other digital-, or “crypto-”, currencies have grown together with their market capitalization. However, a systematic investigation of the relationship between online attention and market dynamics, across multiple digital currencies, is still lacking. Here, we quantify the interplay between the attention towards digital currencies in Wikipedia and their market performance. We consider the entire edit history of currency-related pages and their view history from July 2015. First, we quantify the evolution of the cryptocurrency presence in Wikipedia by analyzing the editorial activity and the network of co-edited pages. We find that a small community of tightly connected editors is responsible for most of the production of information about cryptocurrencies in Wikipedia. Then, we show that a simple trading strategy informed by Wikipedia views performs better, in terms of returns on investment, than baseline strategies for most of the covered period. Our results contribute to the recent literature on the interplay between online information and investment markets, and we anticipate it will be of interest for researchers as well as investors.

Keywords: Bitcoin, Cryptocurrency Market, Wikipedia, Complex networks

JEL Classification: C00, C1, E26, E42, E51, F24, J33, O17

Suggested Citation

ElBahrawy, Abeer and Alessandretti, Laura and Baronchelli, Andrea, Wikipedia and Digital Currencies: Interplay Between Collective Attention and Market Performance (March 4, 2019). Available at SSRN: https://ssrn.com/abstract=3346632 or http://dx.doi.org/10.2139/ssrn.3346632

Abeer ElBahrawy (Contact Author)

City University London, School of Engineering and Mathematical Sciences ( email )

Northampton Square
London, EC1V 0HB
United Kingdom

The Alan Turing Institute ( email )

British Library, 96 Euston Road
London, NW12DB
United Kingdom

Laura Alessandretti

City University London, School of Engineering and Mathematical Sciences ( email )

Northampton Square
London, EC1V 0HB
United Kingdom

Andrea Baronchelli

City University London - School of Engineering and Mathematical Sciences ( email )

Northampton Square
London, EC1V 0HB
United Kingdom

City University London ( email )

Northampton Square
London, EC1V OHB
United Kingdom

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

Paper statistics

Downloads
39
Abstract Views
400
PlumX Metrics