Influencers, Inefficiency and Fraud – The Bitcoin Price Discovery Network Under the Microscope
61 Pages Posted: 25 Apr 2022
Date Written: March 30, 2022
We present a TriSNAR modeling framework for understanding the dynamic interactions of multiple markets for Bitcoin trading, including market efficiency, and for identifying influential exchanges in the global trading network. We consider two types of influential exchanges from the perspectives of investors, regulators, and policymakers: exchanges that are market leaders and exchanges potentially used for market manipulation. Among 194 Bitcoin exchanges, we find that exchange Kraken was the leading exchange prior to the market frenzy of 2017. We also find a fraud-related exchange (Bitfinex) where some other exchanges display a similar role in the price discovery network than this exchange, raising questions about whether they may also be used for fraudulent activities. In addition, price discovery shows that the Bitcoin exchange network has been decreasing in efficiency from 2015 to 2017, and it has been increasingly efficient since 2018. We investigate the finite sample and asymptotic properties of TriSNAR. Compared to alternative methods, TriSNAR outperforms in terms of accuracy, runtime, and ability to discover multi-market network structures.
Keywords: Influencer Identification, Market Efficiency, Fraud Detection, Structure Detection, Bitcoin Exchanges
JEL Classification: C01, C55, C58, G14
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