Market Making or Market Manipulators of High-Frequency Trading ?
Author:
Bessie O’Dell
Edition:
10th edition (2024/2025)
Keywords:
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Introduction
On the morning of 6 May 2010, the UK was focused on gearing up for its general election day, which ultimately resulted in a hung parliament – the first time that a single political party had not achieved a majority in the House of Commons since 1974 (The Editors of Encyclopaedia Britannica, 2025). Across the Atlantic, Wall Street was focused on the mounting Greek debt crisis, and an “unusually turbulent” day for the markets (The U.S. CFTC & U.S. SEC, 2010, p. 1). However, neither of these geopolitical events were to be the triggering factor for a near 1,000-point dive in share prices that day. At 2:42pm EDT, the Dow Jones Industrial Average began to fall rapidly. At one point, it lost almost 9% of its value, in what came to be known as the trillion-dollar “flash crash”. Shockingly, the broad US securities markets shot down by nearly 1 trillion dollars (Poirier, 2012). As a result, for a period of time, financial instruments no longer reflected the underlying value of the companies, and ‘‘system control was lost’’ (Vecellio, 2014, p.1).
What, or who, was held to blame for this dramatic loss? Regulators, the press, public commentators, and financial analysts all pointed to the practice of High-Frequency Trading (HFT). This is an umbrella term for a type of algorithmic trading method in which a large number of securities orders for anything from stocks and shares to cryptocurrency are traded with latencies as low as 10 milliseconds (Jones, 2013; Salkar et al., 2021). In this instance, regulators determined that the catalyst for the “flash crash” was a single high frequency (HF) trader in Kansas City, who was described as “either lazy or sloppy” in executing a large trade on the E-Mini futures market (Poirier, 2012, p. 445; The U.S. CFTC & U.S. SEC, 2010). However, this was not a standalone event. Two years later, an HFT software error at Knight Capital had them “buying high” and “selling low” many times per second, resulting in the company losing $10 million per minute, and $440 million in total (Popper, 2012).