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Algorithmic trading engines versus human traders: Do they behave different in securities markets?

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  • Gomber, Peter
  • Gsell, Markus

Abstract

After exchanges and alternative trading venues have introduced electronic execution mechanisms worldwide, the focus of the securities trading industry shifted to the use of fully electronic trading engines by banks, brokers and their institutional customers. These Algorithmic Trading engines enable order submissions without human intervention based on quantitative models applying historical and real-time market data. Although there is a widespread discussion on the pros and cons of Algorithmic Trading and on its impact on market volatility and market quality, little is known on how algorithms actually place their orders in the market and whether and in which respect this differs form other order submissions. Based on a dataset that for the first time includes a specific flag to enable the identification of orders submitted by Algorithmic Trading engines, the paper investigates the extent of Algorithmic Trading activity and specifically their order placement strategies in comparison to human traders in the Xetra trading system. It is shown that Algorithmic Trading has become a relevant part of overall market activity and that Algorithmic Trading engines fundamentally differ from human traders in their order submission, modification and deletion behavior as they exploit real-time market data and latest market movements.

Suggested Citation

  • Gomber, Peter & Gsell, Markus, 2009. "Algorithmic trading engines versus human traders: Do they behave different in securities markets?," CFS Working Paper Series 2009/10, Center for Financial Studies (CFS).
    • Handle: RePEc:zbw:cfswop:200910
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    References listed on IDEAS

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    1. Markus K. Brunnermeier & Lasse Heje Pedersen, 2005. "Predatory Trading," Journal of Finance, American Finance Association, vol. 60(4), pages 1825-1863, August.
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    4. Gsell, Markus, 2008. "Assessing the impact of algorithmic trading on markets: A simulation approach," CFS Working Paper Series 2008/49, Center for Financial Studies (CFS).
    5. P. Gomber & M. Gsell, 2006. "Catching Up with Technology - The Impact of Regulatory Changes on ECNs/MTFs and the Trading Venue Landscape in Europe," Competition and Regulation in Network Industries, Intersentia, vol. 7(4), pages 535-558, December.
    6. Terrence Hendershott & Charles M. Jones & Albert J. Menkveld, 2011. "Does Algorithmic Trading Improve Liquidity?," Journal of Finance, American Finance Association, vol. 66(1), pages 1-33, February.
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    Citations

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    Cited by:

    1. Le, Anh Tu & Le, Thai-Ha & Liu, Wai-Man & Fong, Kingsley Y., 2020. "Multiple duration analyses of dynamic limit order placement strategies and aggressiveness in a low-latency market environment," International Review of Financial Analysis, Elsevier, vol. 72(C).
    2. Ji-Yong Seo & Sangmi Chai, 2013. "The role of algorithmic trading systems on stock market efficiency," Information Systems Frontiers, Springer, vol. 15(5), pages 873-888, November.
    3. Juraj Hruška, 2016. "Aggressive and Defensive High-Frequency Trading and its Impact on Liquidity of German Stock Market," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 64(6), pages 1911-1918.
    4. Michael Chlistalla & Marco Lutat, 2011. "Competition in securities markets: the impact on liquidity," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 25(2), pages 149-172, June.
    5. He, Peng William & Jarnecic, Elvis & Liu, Yubo, 2015. "The determinants of alternative trading venue market share: Global evidence from the introduction of Chi-X," Journal of Financial Markets, Elsevier, vol. 22(C), pages 27-49.
    6. Hasbrouck, Joel & Saar, Gideon, 2013. "Low-latency trading," Journal of Financial Markets, Elsevier, vol. 16(4), pages 646-679.

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    More about this item

    Keywords

    Electronic Markets; Algorithmic Trading; Order Submission; Securities Trading;
    All these keywords.

    JEL classification:

    • D0 - Microeconomics - - General

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