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A model for unpacking big data analytics in high-frequency trading

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  • Seddon, Jonathan J.J.M.
  • Currie, Wendy L.

Abstract

This study develops a conceptual model of the 7V′s of big data analytics to gain a deeper understanding of the strategies and practices of high-frequency trading (HFT) in financial markets. HFT is computerized trading using proprietary algorithms. Empirical data collected from HFT firms and regulators in the US and UK reveals competitive asymmetries between HFTs and low-frequency traders (LFTs) operating more traditional forms of market trading. These findings show that HFT gains extensive market advantages over LFT due to significant investment in advanced technological architecture. Regulators are challenged to keep pace with HFT as different priorities to the 7V′s are given in pursuit of a short term market strategy. This research has implications for regulators, financial practitioners and investors as the technological arms race is fundamentally changing the nature of global financial markets.

Suggested Citation

  • Seddon, Jonathan J.J.M. & Currie, Wendy L., 2017. "A model for unpacking big data analytics in high-frequency trading," Journal of Business Research, Elsevier, vol. 70(C), pages 300-307.
  • Handle: RePEc:eee:jbrese:v:70:y:2017:i:c:p:300-307
    DOI: 10.1016/j.jbusres.2016.08.003
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    References listed on IDEAS

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    Cited by:

    1. repec:spr:gjofsm:v:18:y:2017:i:3:d:10.1007_s40171-017-0159-3 is not listed on IDEAS
    2. repec:eee:proeco:v:191:y:2017:i:c:p:97-112 is not listed on IDEAS
    3. Adamantios Ntakaris & Martin Magris & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2017. "Benchmark Dataset for Mid-Price Forecasting of Limit Order Book Data with Machine Learning Methods," Papers 1705.03233, arXiv.org, revised Aug 2018.

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    Keywords

    Big data; HFT; Latency; Asymmetry; Strategies;

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