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Make-take decisions under high-frequency trading competition

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  • Bernales, Alejandro

Abstract

The make-take preferences of investors depend on high-frequency trading (HFT) competition, under which HFT firms endogenously acquire speed and informational advantages. In the case where there are many HFT firms in the market, they compete more through limit orders; meanwhile, in the case with few HFT firms, they compete more through market orders that “pick-off" limit orders coming from the big crowd of slow traders. In the former (latter) case, additional HFT competition improves (damage) liquidity. In both cases, HFT competition improves informational efficiency and reduces microstructure noise. Finally, I use the model to analyze potential regulations under HFT competition.

Suggested Citation

  • Bernales, Alejandro, 2019. "Make-take decisions under high-frequency trading competition," Journal of Financial Markets, Elsevier, vol. 45(C), pages 1-18.
    • Handle: RePEc:eee:finmar:v:45:y:2019:i:c:p:1-18
    DOI: 10.1016/j.finmar.2019.05.001
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    References listed on IDEAS

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    Cited by:

    1. Bernales, Alejandro & Ladley, Daniel & Litos, Evangelos & Valenzuela, Marcela, 2021. "Dark trading and alternative execution priority rules," LSE Research Online Documents on Economics 118866, London School of Economics and Political Science, LSE Library.
    2. Breckenfelder, Johannes, 2024. "Competition among high-frequency traders and market quality," Journal of Economic Dynamics and Control, Elsevier, vol. 166(C).
    3. Goudarzi, Mostafa & Bazzana, Flavio, 2023. "Identification of high-frequency trading: A machine learning approach," Research in International Business and Finance, Elsevier, vol. 66(C).
    4. Chih-Chen Hsu & Chung-Gee Lin & Tsung-Jung Kuo, 2020. "Pricing of Arithmetic Asian Options under Stochastic Volatility Dynamics: Overcoming the Risks of High-Frequency Trading," Mathematics, MDPI, vol. 8(12), pages 1-16, December.
    5. Qixuan Luo & Shijia Song & Handong Li, 2023. "Research on the Effects of Liquidation Strategies in the Multi-asset Artificial Market," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1721-1750, December.
    6. Ge, Hengshun & Yang, Haijun & Doukas, John A., 2024. "The optimal strategies of competitive high-frequency traders and effects on market liquidity," International Review of Economics & Finance, Elsevier, vol. 91(C), pages 653-679.
    7. Ladley, Daniel, 2020. "The high frequency trade off between speed and sophistication," Journal of Economic Dynamics and Control, Elsevier, vol. 116(C).
    8. Sagade, Satchit & Scharnowski, Stefan & Theissen, Erik & Westheide, Christian, 2024. "A tale of two cities: Inter-market latency and fast-trader competition," SAFE Working Paper Series 430, Leibniz Institute for Financial Research SAFE.
    9. Ekinci, Cumhur & Ersan, Oğuz, 2022. "High-frequency trading and market quality: The case of a “slightly exposed” market," International Review of Financial Analysis, Elsevier, vol. 79(C).

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    More about this item

    Keywords

    Make-take decisions; High-frequency trading competition; Limit order market; Market quality; Welfare;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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