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High-frequency trading: Order-based innovation or manipulation?

Author

Listed:
  • Viktoria Dalko

    (Hult International Business School)

  • Michael H. Wang

    (Research Institute of Comprehensive Economics)

Abstract

High-frequency trading (HFT) is a financial innovation that focuses on order flow and relies on quickly evolving information and communication technology. The innovation is successful, and HFT is highly and consistently profitable. However, the Flash Crash on 6 May 2010 exposed the unfamiliar side of HFT, thus illuminating the emergent need to unveil the negative impact that HFT has on other investors and the market. This paper examines data regarding quote-stuffing, spoofing, and market making provided by high-frequency (HF) traders, based on the increasing empirical literature. It first defines order-based manipulation (OBM) as the framework under which quote-stuffing, spoofing, and HF market making find common ground. It then provides details regarding how OBM is displayed in the three manipulation tactics. In essence, they all seek and exercise monopoly power in trading albeit through different ways of achieving it. The shared purpose is to gain monopolistic profit. The essence and common purpose explain why HF traders are not net liquidity providers, contrary to some proponents’ conclusions. Rather, this paper points out the three consequences that HF traders have brought to the market, i.e. increased volatility, increased frequency of unfairness, and instability potential. Recent regulatory improvement and completed prosecutions against manipulative HFT strategies justify the analysis.

Suggested Citation

  • Viktoria Dalko & Michael H. Wang, 2020. "High-frequency trading: Order-based innovation or manipulation?," Journal of Banking Regulation, Palgrave Macmillan, vol. 21(4), pages 289-298, December.
    • Handle: RePEc:pal:jbkreg:v:21:y:2020:i:4:d:10.1057_s41261-019-00115-y
    DOI: 10.1057/s41261-019-00115-y
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    References listed on IDEAS

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    Cited by:

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    2. 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).

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