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Detection of algorithmic trading

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  • Bogoev, Dimitar
  • Karam, Arzé

Abstract

We develop a new approach to reflect the behavior of algorithmic traders. Specifically, we provide an analytical and tractable way to infer patterns of quote volatility and price momentum consistent with different types of strategies employed by algorithmic traders, and we propose two ratios to quantify these patterns. Quote volatility ratio is based on the rate of oscillation of the best ask and best bid quotes over an extremely short period of time; whereas price momentum ratio is based on identifying patterns of rapid upward or downward movement in prices. The two ratios are evaluated across several asset classes. We further run a two-stage Artificial Neural Network experiment on the quote volatility ratio; the first stage is used to detect the quote volatility patterns resulting from algorithmic activity, while the second is used to validate the quality of signal detection provided by our measure.

Suggested Citation

  • Bogoev, Dimitar & Karam, Arzé, 2017. "Detection of algorithmic trading," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 484(C), pages 168-181.
    • Handle: RePEc:eee:phsmap:v:484:y:2017:i:c:p:168-181
    DOI: 10.1016/j.physa.2017.04.157
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    References listed on IDEAS

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    1. Upson, James & Van Ness, Robert A., 2017. "Multiple markets, algorithmic trading, and market liquidity," Journal of Financial Markets, Elsevier, vol. 32(C), pages 49-68.
    2. Pastor, Lubos & Stambaugh, Robert F., 2003. "Liquidity Risk and Expected Stock Returns," Journal of Political Economy, University of Chicago Press, vol. 111(3), pages 642-685, June.
    3. Markus K. Brunnermeier & Lasse Heje Pedersen, 2005. "Predatory Trading," Journal of Finance, American Finance Association, vol. 60(4), pages 1825-1863, August.
    4. Hasbrouck, Joel & Saar, Gideon, 2013. "Low-latency trading," Journal of Financial Markets, Elsevier, vol. 16(4), pages 646-679.
    5. Albert J. Menkveld, 2016. "The Economics of High-Frequency Trading: Taking Stock," Annual Review of Financial Economics, Annual Reviews, vol. 8(1), pages 1-24, October.
    6. Jonathan Brogaard & Terrence Hendershott & Ryan Riordan, 2014. "High-Frequency Trading and Price Discovery," The Review of Financial Studies, Society for Financial Studies, vol. 27(8), pages 2267-2306.
    7. 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.
    8. O’Hara, Maureen, 2015. "High frequency market microstructure," Journal of Financial Economics, Elsevier, vol. 116(2), pages 257-270.
    9. Hagströmer, Björn & Nordén, Lars, 2013. "The diversity of high-frequency traders," Journal of Financial Markets, Elsevier, vol. 16(4), pages 741-770.
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