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There are many people that criticize Wall Street for turning more and more to tools that utilize artificial intelligence when it comes to trading. While many have characterized Wall Street as “greedy” for decades, many have particularly criticized the fact that they resort to high-frequency trading, which some even believe should be illegal. It appears as though the cryptocurrency markets are also resorting to trading bots to maximize profit, as well.

Researchers at Cornell Tech have concluded that there is a significant amount of high-frequency bots on some of the decentralized exchanges that many cryptocurrency investors and traders use daily. This is a significant blow to the perception that blockchain allows for more fair and transparent markets, considering that many point out that Wall Street and its high-frequency tactics make it unfair for the average trader or investor. The report also pointed out that there are still significant opportunities to profit through arbitrage in the cryptocurrency markets, as well.

The paper was titled “Flash Boys 2.0: Frontrunning, Transaction Reordering, and Consensus Instability in Decentralized Exchanges”. The research was conducted on several well-known decentralized exchanges, including Token Store, EtherDelta, and the Kyber Network.

For those who might not be aware, the title of the paper is a reference to the book, Flash Boys. Flash Boys: A Wall Street Revolt by Michael Lewis, a financial journalist and bestselling author. The book essentially concludes that technology has helped to rig the stock market for the wealthy.

The paper pointed out that this technology often exploits human error, and that the flawed designs of cryptocurrency exchanges can often lead to orders being “frontrun”. This occurs when there is an informational symmetry, where certain individuals have access to privileged user information and exploit it for their own profit. There are even situations where trading bots profit off human typographical errors when it comes to cryptocurrency buy/sell orders.

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