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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.

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  • 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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    7. Patrick Mikalef & Ilias O. Pappas & John Krogstie & Michail Giannakos, 2018. "Big data analytics capabilities: a systematic literature review and research agenda," Information Systems and e-Business Management, Springer, vol. 16(3), pages 547-578, August.
    8. 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.
    9. Anca Ioana Iacob (Troto), 2021. "Study on Ethics and Integrity in the Use of Big Data in Analysis and Research," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 772-781, August.
    10. 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.
    11. 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.
    12. 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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    14. 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.
    15. 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.
    16. 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.
    17. Pournader, Mehrdokht & Ghaderi, Hadi & Hassanzadegan, Amir & Fahimnia, Behnam, 2021. "Artificial intelligence applications in supply chain management," International Journal of Production Economics, Elsevier, vol. 241(C).
    18. 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.
    19. Alberto Bertello & Alberto Ferraris & Stefano Bresciani & Paola Bernardi, 2021. "Big data analytics (BDA) and degree of internationalization: the interplay between governance of BDA infrastructure and BDA capabilities," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 25(4), pages 1035-1055, December.
    20. 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).
    21. 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.
    22. 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.
    23. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2017. "A multidisciplinary perspective of big data in management research," International Journal of Production Economics, Elsevier, vol. 191(C), pages 97-112.

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