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Is high-frequency trading tiering the financial markets?

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  • Virgilio, Gianluca

Abstract

Some academic research has identified the possibility of High-Frequency Trading (HFT) creating a two tier market, in which the fast traders mostly deal with each other at most favourable prices and spread, leaving the slower investors to share the least profitable deals. Yet, although intriguing, this view has been seldom quantitatively investigated − and that is the gap found in previous research. A computer simulation has been produced to mimic the behaviour of both slow and fast traders, each category showing characteristics consistent with their behaviour on the markets. In particular, High-Frequency (HF) traders show their preference for aggressive orders when the bid-ask spread is tight and are less aggressive when spread is wide. The Low-Frequency (LF) traders are then forced to live with the remaining deals, hoping to profit from longer term price movements. The purpose of this piece of research is to verify whether HF traders (HFTs) tend to deal with each other and, something not investigated by previous studies, if LF traders also mainly restrict their trading with other slow traders.

Suggested Citation

  • Virgilio, Gianluca, 2017. "Is high-frequency trading tiering the financial markets?," Research in International Business and Finance, Elsevier, vol. 41(C), pages 158-171.
    • Handle: RePEc:eee:riibaf:v:41:y:2017:i:c:p:158-171
    DOI: 10.1016/j.ribaf.2017.04.031
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    References listed on IDEAS

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    1. Didier SORNETTE & Susanne VON DER BECKE, 2011. "Crashes and High Frequency Trading," Swiss Finance Institute Research Paper Series 11-63, Swiss Finance Institute.
    2. Thierry Foucault & Ohad Kadan & Eugene Kandel, 2013. "Liquidity Cycles and Make/Take Fees in Electronic Markets," Journal of Finance, American Finance Association, vol. 68(1), pages 299-341, February.
    3. Menkveld, Albert J., 2013. "High frequency trading and the new market makers," Journal of Financial Markets, Elsevier, vol. 16(4), pages 712-740.
    4. Didier SORNETTE & Susanne VON DER BECKE, 2011. "Crashes and High Frequency Trading," Swiss Finance Institute Research Paper Series 11-64, Swiss Finance Institute.
    5. Andrei A. Kirilenko & Andrew W. Lo, 2013. "Moore's Law versus Murphy's Law: Algorithmic Trading and Its Discontents," Journal of Economic Perspectives, American Economic Association, vol. 27(2), pages 51-72, Spring.
    6. Hasbrouck, Joel & Saar, Gideon, 2013. "Low-latency trading," Journal of Financial Markets, Elsevier, vol. 16(4), pages 646-679.
    7. Robert A. Jarrow & Philip Protter, 2012. "A Dysfunctional Role Of High Frequency Trading In Electronic Markets," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 15(03), pages 1-15.
    8. Gsell, Markus, 2008. "Assessing the impact of algorithmic trading on markets: A simulation approach," CFS Working Paper Series 2008/49, Center for Financial Studies (CFS).
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    Cited by:

    1. Bazzana, Flavio & Collini, Andrea, 2020. "How does HFT activity impact market volatility and the bid-ask spread after an exogenous shock? An empirical analysis on S&P 500 ETF," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    2. Yang, Haijun & Ge, Hengshun & Luo, Ying, 2020. "The optimal bid-ask price strategies of high-frequency trading and the effect on market liquidity," Research in International Business and Finance, Elsevier, vol. 53(C).
    3. Sánchez Serrano Antonio, 2020. "High-Frequency Trading and Systemic Risk: A Structured Review of Findings and Policies," Review of Economics, De Gruyter, vol. 71(3), pages 169-195, December.

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    More about this item

    Keywords

    Agent-Base Model; High-frequency trading; Simulation; Split; Tier;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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