The Flash Crash: The Impact of High Frequency Trading on an Electronic Market

Kirilenko, Andrei; Kyle, Albert S.; Samadi, Mehrdad; Tuzun, Tugkan

doi:10.2139/ssrn.1686004

Cited by 377 publications

(356 citation statements)

References 15 publications

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“…The major concerns are the quality of the liquidity provided by HF traders and whether they increase volatility. Kirilenko et al (2011) find evidence that instead of supplying liquidity, some HF traders withdrew from the market and some demanded liquidity during the Flash Crash on May 6, 2010. Hasbrouck and Saar (2009) document the "fleeting" nature of many limit orders in electronic markets and point out the liquidity provided by HF traders is short-lived.…”

Section: Related Literaturementioning

confidence: 96%

“…Since HF traders do not have an affirmative obligation to provide liquidity, their trading is opportunistic in nature, and the liquidity they create may disappear quickly when it is most needed on the market. Kirilenko et al (2011) and Easley et al (2011a) both note that during the Flash Crash of May 6, 2010, many HF traders withdrew from the market while others turned into liquidity demanders. In the context of institutional trading, an open question is whether HFT is a reliable source of liquidity when liquidity is most demanded by institutional investors.…”

Section: Introductionmentioning

confidence: 99%

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A Blessing or a Curse? The Impact of High Frequency Trading on Institutional Investors

Tong

2013

SSRN Journal

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Combining data on high frequency trading (HFT) activities of a randomly selected sample of 120 stocks and data on institutional trades, I find that HFT increases the trading costs of traditional institutional investors. One standard deviation increase in the intensity of HFT activities increases institutional execution shortfall costs by a third. Further analysis suggests that HFT represents as an ephemeral and extra-expensive source of liquidity provision when demand and supply among institutional investors are imbalanced, and that the impact on institutional trading costs is most pronounced when high frequency (HF) traders engage in directional strategies (e.g., front-running).I perform various analyses to rule out an alternative explanation that HF traders are attracted to stocks that have high trading costs. First, HFT is most active on liquid stocks. Second, the results are robust to the controls for stable stock liquidity characteristics and events that might jointly affect HFT and trading costs. Third, an analysis of the HFT behavior around the temporary short selling ban in September 2008 highlights the opportunistic nature of liquidity provision by HF traders. Finally, Granger causality tests show that intensive HFT activity significantly contributes to institutional trading costs, but not vice versa.

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Section: Related Literaturementioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

A Blessing or a Curse? The Impact of High Frequency Trading on Institutional Investors

Tong

2013

SSRN Journal

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“…Whereas high frequency traders are often a significant source of liquidity in the financial markets, Kirilenko et al (2011) conclude that high frequency traders" behavior on the day of the flash crash exacerbated market volatility. Consistent with this notion, the CFTC-SEC (2010) report on the flash crash finds evidence that some high frequency traders aggressively sold shares during the crash, while others scaled back or stopped trading altogether.…”

Section: Trade Executionmentioning

confidence: 97%

“…Academic studies of the flash crash tend to focus on the role of high frequency traders. While stopping short of blaming high frequency traders directly, Easley et al (2011) and Kirilenko et al (2011) conclude that they did contribute to the extreme market volatility witnessed on the day of the crash. Lee et al (2011) suggest that the flash crash resulted from systematic traders with similar trading strategies, while Yu (2011) finds evidence that contrarian trading strategies helped to mitigate the effects of the flash crash.…”

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confidence: 98%

The flash crash: An examination of shareholder wealth and market quality

Boulton

Braga‐Alves

Kulchania

2014

Journal of Financial Intermediation

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We investigate stock returns, market quality, and options market activity around the flash crash of May 6, 2010. Abnormal returns are negative on the day of and the day after the flash crash for stocks that had trades that executed during the crash subsequently cancelled by either Nasdaq or NYSE Arca. Consistent with studies that suggest that other sources of liquidity withdrew from the markets during the flash crash, we find that the fraction of trades executed by the NYSE increases during this volatile period. Market quality deteriorates following the flash crash as bidask spreads increase and quote depths decrease. Evidence from the options markets indicates that investor uncertainty increased around the time of the crash and remained elevated for several days afterwards.

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“…These two time scales reflect two different types of traders: the slow scale for the ordinary investors and the fast scale for the others. They are referred to as fundamental traders and high-frequency traders, respectively, in a recent empirical work of Kirilenko et al [43]. They show that these two different types of traders displayed statistically different behavior and played different roles during the flash crash of May 2010.…”

Section: Optimal Placement Vs Optimal Executionmentioning

confidence: 99%

Optimal Placement in a Limit Order Book

Guo¹

2013

Theory Driven by Influential Applications

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Please scroll down for article-it is on subsequent pagesINFORMS is the largest professional society in the world for professionals in the fields of operations research, management science, and analytics. For more information on INFORMS, its publications, membership, or meetings visit http://www.informs.org Abstract Algorithmic trading refers to the automatic and rapid trading of large quantities with orders specified and implemented by an algorithm. Roughly speaking, algorithmic trading is based on two different time scales: the daily or weekly scale and a smaller (10-to 100-second) time scale. These two time scales essentially reflect the two steps by which the traders slice and place orders. The first step is to optimally slice big orders into smaller ones on a daily basis with the goal to minimize the price impact and/or to maximize the expected utility; the second step is to optimally place the orders within seconds. The former is the well-known optimal execution problem, and the latter is the much less studied optimal placement problem. This paper reviews several simple models and approaches for the optimal placement problem. Several most relevant statistical issues are presented, together with a brief discussion on the key differences between the system of limit order book and the multiclass queues with reneging.

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The Flash Crash: The Impact of High Frequency Trading on an Electronic Market

Cited by 377 publications

References 15 publications

A Blessing or a Curse? The Impact of High Frequency Trading on Institutional Investors

A Blessing or a Curse? The Impact of High Frequency Trading on Institutional Investors

The flash crash: An examination of shareholder wealth and market quality

Optimal Placement in a Limit Order Book

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