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


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


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

    1. Donald MacKenzie, 2006. "An Engine, Not a Camera: How Financial Models Shape Markets," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262134608, January.
    2. Yacine Aït-Sahalia & Mehmet Saglam, 2013. "High Frequency Traders: Taking Advantage of Speed," NBER Working Papers 19531, National Bureau of Economic Research, Inc.
    3. 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.
    4. Jonathan Brogaard & Terrence Hendershott & Ryan Riordan, 2014. "High-Frequency Trading and Price Discovery," Review of Financial Studies, Society for Financial Studies, vol. 27(8), pages 2267-2306.
    5. Constantiou, Ioanna D & Kallinikos, Jannis, 2015. "New games, new rules: big data and the changing context of strategy," LSE Research Online Documents on Economics 63017, London School of Economics and Political Science, LSE Library.
    6. Alex Preda, 2007. "The Sociological Approach To Financial Markets," Journal of Economic Surveys, Wiley Blackwell, vol. 21(3), pages 506-533, July.
    7. Mike Bennett, 2013. "The financial industry business ontology: Best practice for big data," Journal of Banking Regulation, Palgrave Macmillan, vol. 14(3-4), pages 255-268, July.
    8. Alnoor Bhimani & Leslie Willcocks, 2014. "Digitisation, 'Big Data' and the transformation of accounting information," Accounting and Business Research, Taylor & Francis Journals, vol. 44(4), pages 469-490, August.
    9. Menkveld, Albert J., 2013. "High frequency trading and the new market makers," Journal of Financial Markets, Elsevier, vol. 16(4), pages 712-740.
    10. Alain P. Chaboud & Benjamin Chiquoine & Erik Hjalmarsson & Clara Vega, 2014. "Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market," Journal of Finance, American Finance Association, vol. 69(5), pages 2045-2084, October.
    11. Jonathan Brogaard & Björn Hagströmer & Lars Nordén & Ryan Riordan, 2015. "Trading Fast and Slow: Colocation and Liquidity," Review of Financial Studies, Society for Financial Studies, vol. 28(12), pages 3407-3443.
    12. Hoffmann, Peter, 2014. "A dynamic limit order market with fast and slow traders," Journal of Financial Economics, Elsevier, vol. 113(1), pages 156-169.
    13. Cooper, Ricky & Davis, Michael & Van Vliet, Ben, 2016. "The Mysterious Ethics of High-Frequency Trading," Business Ethics Quarterly, Cambridge University Press, vol. 26(01), pages 1-22, January.
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    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,, revised Aug 2018.

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    Big data; HFT; Latency; Asymmetry; Strategies;


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