An Experimental Study of Cryptocurrency Market Dynamics
Name
1801.05831.pdf
Description
Accepted version
Size
892.73 KB
Format
Adobe PDF
Checksum (MD5)
deab5e741b8764856322eb0365f692ff
Author(s)
Krafft, Peter M.
Della Penna, Nicolas
Pentland, Alex Paul
Date Issued
2018
Journal
Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems
Publisher
Association for Computing Machinery (ACM)
Citation
Krafft, Peter M., et al. “An Experimental Study of Cryptocurrency Market Dynamics.” Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems - CHI ’18, April, 2018, Montreal, Canada, ACM Press, 2018.
Version
Author's final manuscript
Abstract
As cryptocurrencies gain popularity and credibility, marketplaces for cryptocurrencies are growing in importance. Understanding the dynamics of these markets can help to assess how viable the cryptocurrnency ecosystem is and how design choices affect market behavior. One existential threat to cryptocurrencies is dramatic fluctuations in traders' willingness to buy or sell. Using a novel experimental methodology, we conducted an online experiment to study how susceptible traders in these markets are to peer influence from trading behavior. We created bots that executed over one hundred thousand trades costing less than a penny each in 217 cryptocurrencies over the course of six months. We find that individual "buy" actions led to short-term increases in subsequent buy-side activity hundreds of times the size of our interventions. From a design perspective, we note that the design choices of the exchange we study may have promoted this and other peer influence effects, which highlights the potential social and economic impact of HCI in the design of digital institutions. Keywords: Cryptocurrencies; online markets; peer influence; computational social science; online field experiments; digital institutions; design; market design
MIT Department
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
Massachusetts Institute of Technology. Media Laboratory
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3173574.3174179
