Algorithmic Trading and the Market for Liquidity

Hendershott, Terrence; Riordan, Ryan

doi:10.2139/ssrn.2001912

Cited by 118 publications

(143 citation statements)

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“…Studies such as Hendershott, Jones and Menkveld (2011), Hendershott and Riordan (2012), Brogaard, Hendershott and Riordan (2013) and Baron, Brogaard and Kirilenko (2012), attempt to disentangle the incremental effect of algorithmic trading and HFT from other changes in the equity markets by isolating market structure changes that facilitate high-frequency trading. This effort is difficult because it is not possible to directly observe whether a particular order is generated by an HFT computer algorithm, as opposed to just being any other kind of automated trade that does not rely on human intermediaries.…”

Section: Effects Of Hft On Market Liquidity and Transaction Costsmentioning

confidence: 99%

“…Therefore, proxies for algorithmic trading and the HFT portion thereof have been developed. These include the rate of electronic message traffic normalized by trading volume as used by Hendershott, Jones and Menkveld (2011) and Viljoen, Westerholm, and Zheng (2014), the use of proprietary data to identify specific HFTs in the data as in Brogaard, Hendershott, Hunt, and Ysusi (2014), or the use of account-level trade-by-trade data on certain contracts and schemes for classifying traders into various high-frequency categories, based on their trading volume and inventory management; see Hendershott and Riordan (2012), Brogaard, Hendershott and Riordan (2013) and Baron, Brogaard and Kirilenko (2012).…”

Section: Effects Of Hft On Market Liquidity and Transaction Costsmentioning

confidence: 99%

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Computerized and High-Frequency Trading

Goldstein

Kumar²,

Graves³

2014

SSRN Journal

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Section: Effects Of Hft On Market Liquidity and Transaction Costsmentioning

confidence: 99%

Section: Effects Of Hft On Market Liquidity and Transaction Costsmentioning

confidence: 99%

Computerized and High-Frequency Trading

Goldstein

Kumar²,

Graves³

2014

SSRN Journal

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“…The authors are able to follow these traders, and they conclude that the HF traders earn abnormal positive profits. Hendershott and Riordan (2013) examine algorithmic trading effects on market liquidity. HF trading has led to overinvestment in trading technology, which Harris (2013) compares to an arms race.…”

Section: Prior Researchmentioning

confidence: 99%

High Speed Equities Trading: 1993–2012

Stoll

2014

Asia-Pac J of Fin Stud

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High speed trading has drawn the attention of regulators who fear that such trading harms markets and leads to excessive speculation.1 The Flash Crash of May 6, 2010 is taken as evidence of the potential harmful effects of high frequency trading. On the other hand, some view high frequency trading as a manifestation of technological advances that have reduced the optimal trade size and improved order routing. From that perspective, high speed trading is a continuation of a long-standing trend to more rapid and more efficient trading. This study shows that the speed of trading has changed dramatically. The average number of trades per day for large cap New York Stock Exchange stocks has risen from about 500 to more than 40 000 in the period 1993-2011. At the same time, the average trade size has fallen from 1600 shares to 200 shares. The ultimate sources of these changes are the technology that has automated almost all aspects of trading and the regulatory developments that have helped reduce bid-ask spreads and made markets more accessible. The result of these developments is that markets are considerably more liquid and less costly. High frequency traders draw on this liquidity and also contribute to it.

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“…3 See, for instance, Hendershott, Jones, and Menkveld (2011), Hendershott and Riordan (2013), Brogaard, Hendershott, and Riordan (2014), and Chaboud, Chiquoine, Hjalmarsson, and Vega (2014). a large segment of market participants is provided by the "Quant Meltdown" in August 2007.…”

Section: Introductionmentioning

confidence: 99%

Interactions among High-Frequency Traders

Benos¹,

Brugler²,

Hjalmarsson³

et al. 2017

J. Financ. Quant. Anal.

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Using unique transactions data for individual high-frequency trading (HFT) firms in the U.K. equity market, we examine the extent to which the trading activity of individual HFT firms is correlated with each other and the impact on price efficiency. We find that HFT order flow, net positions, and total volume exhibit significantly higher commonality than those of a comparison group of investment banks. However, intraday HFT order flow commonality is associated with a permanent price impact, suggesting that commonality in HFT activity is information-based and so does not generally contribute to undue price pressure and price dislocations. Keywords: High-Frequency Trading; Correlated Trading Strategies; Price Discovery. JEL Classification: G10, G12, G14. * Benos is with the Bank of England, Threadneedle Street, London, EC2R 8AH, U.K.; Brugler is with the University of Melbourne, Department of Finance, Level 11, 198 Berkeley St, Victoria 3010, Australia; Hjalmarsson is with the University of Gothenburg, Department of Economics, Centre for Finance, Vasagatan 1, SE 405 30 Gothenburg, Sweden; and Zikes is with the Division of Financial Stability, Federal Reserve Board, 1801 K Street NW, Washington, DC 20037. Please address comments to the authors via e-mail at Evangelos. Benos@bankofengland.co.uk, james.brugler@unimelb.edu.au, erik.hjalmarsson@economics.gu.se, and Filip.Zikes@frb.gov. We are grateful to Satchit Sagade for his help in cleaning and processing the data. Helpful comments were provided by Monica Billio, Björn Hagströmer, Edwin Schooling Latter, Nick Vause, Graham Young, and seminar participants at the Bank of England, Bank of Greece, Copenhagen Business School, the Federal Reserve Board, the U.K. Financial Conduct Authority, University of Piraeus, University of York, the conference on The Development of Securities Markets: Trends, Risks and Policies at Bocconi University, the 2015 conference of the International Association of Applied Econometrics, and the 2014 Ioannina Meeting on Applied Economics and Finance. The views in this paper are solely the responsibility of the authors and should not be interpreted as representing either the views of the Bank of England or any of its committees, or the U.K. I IntroductionHigh-frequency trading, where automated computer traders interact at lightningfast speed with electronic trading platforms, has become an important feature of many modern markets. The rapid growth and increased prominence of these ultra fast traders have given rise to concerns regarding their impact on market quality and stability. Recent events, such as the "flash crashes" in U.S. equity markets on May 6, 2010 and U.S. Treasury markets on October 15, 2014, have highlighted such worries. Over the past few years, numerous empirical studies have analyzed the market impact of high-frequency trading (HFT), as well as algorithmic trading (AT) more generally.1,2 With some recent exceptions, most of these studies have analyzed aggregate measures of HFT and AT in various markets. 3 The curre...

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Algorithmic Trading and the Market for Liquidity

Cited by 118 publications

References 45 publications

Computerized and High-Frequency Trading

Computerized and High-Frequency Trading

High Speed Equities Trading: 1993–2012

Interactions among High-Frequency Traders

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