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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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    1. Yacine Aït-Sahalia & Mehmet Saglam, 2013. "High Frequency Traders: Taking Advantage of Speed," NBER Working Papers 19531, National Bureau of Economic Research, Inc.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Menkveld, Albert J., 2013. "High frequency trading and the new market makers," Journal of Financial Markets, Elsevier, vol. 16(4), pages 712-740.
    7. 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.
    8. 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, December.
    9. 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.
    10. Alex Preda, 2007. "The Sociological Approach To Financial Markets," Journal of Economic Surveys, Wiley Blackwell, vol. 21(3), pages 506-533, July.
    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(1), pages 1-22, January.
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    2. 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 Mar 2020.
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    9. Hayajneh, Jamal Abdelrahman .M. & Elayan, Malek Bakheet Haroun & Abdellatif, Mamdouh Abdallah Mohamed & Abubakar, A. Mohammed, 2022. "Impact of business analytics and π-shaped skills on innovative performance: Findings from PLS-SEM and fsQCA," Technology in Society, Elsevier, vol. 68(C).
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    13. Kristoffersen, Eivind & Mikalef, Patrick & Blomsma, Fenna & Li, Jingyue, 2021. "The effects of business analytics capability on circular economy implementation, resource orchestration capability, and firm performance," International Journal of Production Economics, Elsevier, vol. 239(C).
    14. Issam Laguir & Sachin Modgil & Indranil Bose & Shivam Gupta & Rebecca Stekelorum, 2023. "Performance effects of analytics capability, disruption orientation, and resilience in the supply chain under environmental uncertainty," Annals of Operations Research, Springer, vol. 324(1), pages 1269-1293, May.
    15. Aritra Pan & Arun Kumar Misra & David McMillan, 2021. "A comprehensive study on bid-ask spread and its determinants in India," Cogent Economics & Finance, Taylor & Francis Journals, vol. 9(1), pages 1898735-189, January.
    16. Huck, Nicolas, 2019. "Large data sets and machine learning: Applications to statistical arbitrage," European Journal of Operational Research, Elsevier, vol. 278(1), pages 330-342.
    17. Carsten Giebe, 2019. "The Chief Digital Officer – Savior for the Digitalization in German Banks?," Journal of Economic Development, Environment and People, Alliance of Central-Eastern European Universities, vol. 8(3), pages 6-15, September.
    18. Olabode, Oluwaseun E. & Boso, Nathaniel & Hultman, Magnus & Leonidou, Constantinos N., 2022. "Big data analytics capability and market performance: The roles of disruptive business models and competitive intensity," Journal of Business Research, Elsevier, vol. 139(C), pages 1218-1230.
    19. Gangadhar Nayak & Amit Kumar Singh & Dilip Senapati, 2021. "Computational Modeling of Non-Gaussian Option Price Using Non-extensive Tsallis’ Entropy Framework," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1353-1371, April.
    20. Akyildirim, Erdinc & Sensoy, Ahmet & Gulay, Guzhan & Corbet, Shaen & Salari, Hajar Novin, 2021. "Big data analytics, order imbalance and the predictability of stock returns," Journal of Multinational Financial Management, Elsevier, vol. 62(C).
    21. Benjamin Clapham & Martin Haferkorn & Kai Zimmermann, 2023. "The Impact of High-Frequency Trading on Modern Securities Markets," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 65(1), pages 7-24, February.
    22. Sehrish Atif & Shehzad Ahmed & Muhammad Wasim & Bassam Zeb & Zeeshan Pervez & Lorraine Quinn, 2021. "Towards a Conceptual Development of Industry 4.0, Servitisation, and Circular Economy: A Systematic Literature Review," Sustainability, MDPI, vol. 13(11), pages 1-27, June.
    23. Maria Hoffmann Jensen & John Stouby Persson & Peter Axel Nielsen, 2023. "Measuring benefits from big data analytics projects: an action research study," Information Systems and e-Business Management, Springer, vol. 21(2), pages 323-352, June.

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