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High frequency market microstructure

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  • O’Hara, Maureen

Abstract

Markets are different now, transformed by technology and high frequency trading. In this paper, I investigate the implications of these changes for high frequency market microstructure (HFT). I describe the new high frequency world, with a particular focus on how HFT affects the strategies of traders and markets. I discuss some of the gaps that arise when thinking about microstructure research issues in the high frequency world. I suggest that, like everything else in the markets, research must also change to reflect the new realities of the high frequency world. I propose some topics for this new research agenda in high frequency market microstructure.

Suggested Citation

  • O’Hara, Maureen, 2015. "High frequency market microstructure," Journal of Financial Economics, Elsevier, vol. 116(2), pages 257-270.
    • Handle: RePEc:eee:jfinec:v:116:y:2015:i:2:p:257-270
    DOI: 10.1016/j.jfineco.2015.01.003
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    Cited by:

    1. Alexandru-Ioan Stan, 2018. "Computational speed and high-frequency trading profitability: an ecological perspective," Electronic Markets, Springer;IIM University of St. Gallen, vol. 28(3), pages 381-395, August.
    2. Péter Kondor & Gábor Pintér, 2022. "Clients' Connections: Measuring the Role of Private Information in Decentralized Markets," Journal of Finance, American Finance Association, vol. 77(1), pages 505-544, February.
    3. Ryan Garvey & Tao Huang & Fei Wu, 2021. "Is faster or slower trading better? An examination of order type execution speed and costs," European Financial Management, European Financial Management Association, vol. 27(2), pages 326-363, March.
    4. Wen, Zhuzhu & Bouri, Elie & Xu, Yahua & Zhao, Yang, 2022. "Intraday return predictability in the cryptocurrency markets: Momentum, reversal, or both," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    5. Bizzozero, Paolo & Flepp, Raphael & Franck, Egon, 2018. "The effect of fast trading on price discovery and efficiency: Evidence from a betting exchange," Journal of Economic Behavior & Organization, Elsevier, vol. 156(C), pages 126-143.
    6. Ranjan R. Chakravarty & Sudhanshu Pani, 2021. "A Data Paradigm to Operationalise Expanded Filtration: Realized Volatilities and Kernels from Non-Synchronous NASDAQ Quotes and Trades," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(4), pages 617-652, December.
    7. Bart Frijns & Ivan Indriawan & Alireza Tourani‐Rad, 2021. "Quote dynamics of cross‐listed stocks," International Review of Finance, International Review of Finance Ltd., vol. 21(2), pages 497-522, June.
    8. Thomas H. McInish & Olena Nikolsko‐Rzhevska & Alex Nikolsko‐Rzhevskyy & Irina Panovska, 2020. "Fast and slow cancellations and trader behavior," Financial Management, Financial Management Association International, vol. 49(4), pages 973-996, December.
    9. Bogoev, Dimitar & Karam, Arzé, 2017. "Detection of algorithmic trading," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 484(C), pages 168-181.

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    More about this item

    Keywords

    High frequency trading; Market microstructure; Algorithmic trading;
    All these keywords.

    JEL classification:

    • D02 - Microeconomics - - General - - - Institutions: Design, Formation, Operations, and Impact
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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