Billionaire and Shark Tank star Mark Cuban has a prediction on what could be the next thing that rocks the crypto industry.
In a new interview with The Street, Cuban says he believes the next “implosion” in crypto markets will stem from a realization that much of the trading volume on centralized digital asset exchanges is in fact wash trading.
“I think the next possible implosion is the discovery and removal of wash trades on central exchanges. There are supposedly tens of millions of dollars in trades and liquidity for tokens that have very little utilization. I don’t see how they can be that liquid.”
Wash trading is when a single entity makes many different trades to give the appearance of volume, demand and liquidity for an asset and is often used in “pump and dump” schemes in crypto and other markets.
Cuban noted that he doesn’t “have any specifics” to support his guess.
Recently, the National Bureau for Economic Research (NBER) published a working paper studying 29 major crypto exchanges. The study determined that on average, about 70% of volume on unregulated exchanges is wash trading.
The researchers used Benford’s Law, a statistical benchmark used to detect fraud, to analyze trading data from the exchanges. According to the researchers, trading data from most of the major unregulated crypto exchanges indicate “excessive” wash trading.
“We further provide suggestive evidence that wash trading inflates exchange rankings and cryptocurrency prices, in addition to being significantly predicted by market signals such as past cryptocurrency prices and volatility and exchange characteristics such as exchange age and userbase.
As the first comprehensive study of the pervasive crypto wash trading, our paper not only provides a cautionary tale to regulators around the globe but also reminds the readers of the disciplining or screening effects of regulation in emerging industries, the importance of using wash-trading-adjusted volume in certain empirical studies, and the utility of statistical tools and behavioral benchmarks for forensic finance and fraud detection.”
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