New research carried out by Daniele Bianchi and Alexander Dickerson at the University of Warwick suggest well-informed traders with information asymmetries are driving big price swings by timing markets in a manner where others follow in their footsteps.
The information was revealed in the latest draft of a research paper entitled “Trading Volume In Cryptocurrency Markets”, compiled by Assistant Professor of Finance Daniele Bianchi and Alexander Dickerson, a Ph.D. student, both of the Warwick Business School.
The duo looked at intraday price and volume data from CryptoCompare to assert “that the interaction between past volume and returns positively and significantly predicts future returns,” according to a release by the Warwick Business School.
Traders Drive Price Swings
The release noted how the findings remain consistent with exisiting models:
“consistent with existing theoretical models which postulate that informed traders who speculate on their private information are key drivers of the observed price changes.”
Bianchi wrote how “the cryptocurrency market is the perfect environment to exploit asymmetric information,” since its opaque nature gives those with information the ability to “time the market, make money, and drive the prices.”
The authors of the research tracked 26 cryptocurrencies across 150 exchanges to gather information about markets. The cryptoassets were tracked between January 1st, 2017 to May 10th, 2018, covering the boom and bust of the 2017 bull market.
Patterns With Other Asset Classes?
In the conclusion of the report, Bianchi and Dickerson wrote how their empirical evidence helps give more insights into the crypto market by offering comparisons with traditional ones like FX.
They explained how the evolving cryptoasset class “may not necessarily be different from long-established and more mature markets.”
However, previous research by Bianchi found no crypto trading correlations ‘with any economic indicators that investors would base decisions on or with commodities.’
In a working paper called Cryptocurrencies as an Asset Class: An Empirical Assessment, the professor explained how crypto pricing was influenced by previous returns and the emotions and moods of investors.
