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

Author

Listed:
  • Jonathan J.J.M. Seddon

    (Audencia Business School)

  • Wendy L. Currie

    (Audencia Business School)

Abstract

This study develops a conceptual model of the 7 V′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 7 V′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

  • Jonathan J.J.M. Seddon & Wendy L. Currie, 2017. "A model for unpacking big data analytics in high-frequency trading," Post-Print hal-01404316, HAL.
    • Handle: RePEc:hal:journl:hal-01404316
    DOI: 10.1016/j.jbusres.2016.08.003
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    Cited by:

    1. 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.
    2. 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.
    3. Conboy, Kieran & Mikalef, Patrick & Dennehy, Denis & Krogstie, John, 2020. "Using business analytics to enhance dynamic capabilities in operations research: A case analysis and research agenda," European Journal of Operational Research, Elsevier, vol. 281(3), pages 656-672.
    4. Kristoffersen, Eivind & Mikalef, Patrick & Blomsma, Fenna & Li, Jingyue, 2021. "Towards a business analytics capability for the circular economy," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    5. Purva Grover & Arpan Kumar Kar, 2017. "Big Data Analytics: A Review on Theoretical Contributions and Tools Used in Literature," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 18(3), pages 203-229, September.
    6. Zeeshan Ahmed & Shahid Rasool & Qasim Saleem & Mubashir Ali Khan & Shamsa Kanwal, 2022. "Mediating Role of Risk Perception Between Behavioral Biases and Investor’s Investment Decisions," SAGE Open, , vol. 12(2), pages 21582440221, May.
    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. 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).
    9. Irina Bogdana Pugna & Dana Maria Boldeanu & Mirela Gheorghe & Gabriel Cozgarea & Adrian Nicolae Cozgarea, 2022. "Management Perspectives towards the Data-Driven Organization in the Energy Sector," Energies, MDPI, vol. 15(16), pages 1-20, August.
    10. 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).
    11. 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.
    12. 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).
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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).
    20. 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.
    21. 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.
    22. 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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