Price discovery and the cross-section of high-frequency trading

Benos, Evangelos; Sagade, Satchit

doi:10.1016/j.finmar.2016.03.004

Cited by 54 publications

(36 citation statements)

References 27 publications

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“…Hagströmer & Nordén (2013) document strong persistence in types, with about half a dozen HFM types and two dozen HFB types. Benos & Sagade (2016) find the same and further report that both types seem to be informed. Brogaard, Hendershott & Riordan (2014) carefully distinguish between transitory and permanent price changes and find that HFT market orders trade in the direction of permanent price changes, and HFM limit orders opposite to them.…”

Section: Evidencesupporting

confidence: 59%

The Economics of High-Frequency Trading: Taking Stock

Menkveld

2016

Annu. Rev. Financ. Econ.

249

100

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Section: Evidencesupporting

confidence: 59%

The Economics of High-Frequency Trading: Taking Stock

Menkveld

2016

Annu. Rev. Financ. Econ.

249

100

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“…These criteria are also consistent with other schemes used to identify HFTs, such as those in Baron, Brogaard, and Kirilenko (2014), Kirilenko, Kyle, Samadi, and Tuzun (2016), and Korajczyk and Murphy (2016). The resulting data set of HFT activity is very similar to that used by Benos and Sagade (2016).We also use reports on proprietary trades submitted by the 10 largest investment banks (IBs) to compare and contrast the trading activity of the IBs with that of HFTs.8 For the remainder of the paper, we refer to both HFTs and IBs as (trading) firms.Finally, we use quote data from the LSE, obtained via Bloomberg, to reconstruct the top of the order book and to match the ZEN trade reports with the prevailing best bid and ask prices at the time of a given transaction. This allows us to classify trades as either buyer-or seller-initiated, using the usual classification scheme of Lee and Ready (1991).…”

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confidence: 80%

“…Most previous studies have been restricted to using aggregate measures of HFT or AT participation and have focused more on the speed aspects of computer-based trading, and less on the "cross-sectional" aspects. 5 A concurrent study by Boehmer, Li, and Saar (2016) 5 Benos and Sagade (2016), Hagströmer and Nordén (2013), and Hagströmer, Nordén, and Zhang (2014) also make explicit use of the ability to follow individual HFT firms. Their focus is, however, quite different from ours, and mostly on classifying and distinguishing HFTs along market-maker and market-taker lines and assessing the aggregate impact of HFTs on market quality.…”

Section: Related Literaturementioning

confidence: 99%

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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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“…Hasbrouck and Saar, 2013), or message traffic (e.g. Hendershott, Jones and Menkveld, 2011 Benos and Sagade, 2016;). Note that most of these data sets only cover short sample periods (several months at most).…”

Section: Introductionmentioning

confidence: 99%

Algorithmic trading and liquidity: Long term evidence from Austria

Mestel

Murg

Theissen

2018

Finance Research Letters

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We analyze the relation between algorithmic trading and liquidity using a novel data set from the Austrian equity market. Our sample covers almost 4.5 years, it identifies the market share of algorithmic trading at the stock-day level, and it comes from a market that has hitherto not been analyzed. We address the endogeneity problem using an instrumental variables approach. Our results indicate that an increase in the market share of algorithmic trading causes a reduction in quoted and effective spreads while quoted depth and price impacts are unaffected. They are consistent with algorithmic traders on average acting as market makers. Abstract:We analyze the relation between algorithmic trading and liquidity using a novel data set from the Austrian equity market. Our sample covers almost 4.5 years, it identifies the market share of algorithmic trading at the stock-day level, and it comes from a market that has hitherto not been analyzed. We address the endogeneity problem using an instrumental variables approach. Our results indicate that an increase in the market share of algorithmic trading causes a reduction in quoted and effective spreads while quoted depth and price impacts are unaffected. They are consistent with algorithmic traders on average acting as market makers.JEL classification: G10, C58

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Price discovery and the cross-section of high-frequency trading

Cited by 54 publications

References 27 publications

The Economics of High-Frequency Trading: Taking Stock

The Economics of High-Frequency Trading: Taking Stock

Interactions among High-Frequency Traders

Algorithmic trading and liquidity: Long term evidence from Austria

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