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Welfare and Optimal Trading Frequency in Dynamic Double Auctions

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  • Songzi Du
  • Haoxiang Zhu

Abstract

This paper studies the welfare consequence of increasing trading speed in financial markets. We build and solve a dynamic trading model, in which traders receive private information of asset value over time and trade strategically with demand schedules in a sequence of double auctions. A stationary linear equilibrium and its efficiency properties are characterized explicitly in closed form. Infrequent trading (few double auctions per unit of time) leads to a larger market depth in each trading period, but frequent trading allows more immediate asset re-allocation after new information arrives. Under natural conditions, the socially optimal trading frequency coincides with information arrival frequency for scheduled information releases, but can (far) exceed information arrival frequency for stochastic information arrivals. If traders have heterogeneous trading speeds, fast traders prefer the highest feasible trading frequency, whereas slow traders tend to prefer a strictly lower frequency.

Suggested Citation

  • Songzi Du & Haoxiang Zhu, 2014. "Welfare and Optimal Trading Frequency in Dynamic Double Auctions," NBER Working Papers 20588, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:20588
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    Cited by:

    1. Jiho Park, 2024. "Heterogeneous Beliefs Model of Stock Market Predictability," Papers 2406.08448, arXiv.org.
    2. Cespa, Giovanni & Vives, Xavier, 2017. "High frequency trading and fragility," Working Paper Series 2020, European Central Bank.
    3. Franklin Allen & Xian Gu & Julapa Jagtiani, 2021. "A Survey of Fintech Research and Policy Discussion," Review of Corporate Finance, now publishers, vol. 1(3-4), pages 259-339, July.
    4. Albert S. Kyle & Anna Obizhaeva & Yajun Wang, 2016. "Smooth Trading with Overconfidence and Market Power," Working Papers w0226, New Economic School (NES).
    5. Robert Shimer & Gregor Jarosch & Maryam Farboodi, 2016. "Meeting Technologies in Decentralized Asset Markets," 2016 Meeting Papers 844, Society for Economic Dynamics.
    6. Vives, Xavier & Cespa, Giovanni, 2016. "Market Transparency and Fragility," CEPR Discussion Papers 11732, C.E.P.R. Discussion Papers.
    7. Pavan, Alessandro & Vives, Xavier, 2015. "Information, Coordination, and Market Frictions: An Introduction," Journal of Economic Theory, Elsevier, vol. 158(PB), pages 407-426.
    8. Darrell Duffie & Haoxiang Zhu, 2017. "Size Discovery," The Review of Financial Studies, Society for Financial Studies, vol. 30(4), pages 1095-1150.
    9. Albert S Kyle & Anna A Obizhaeva & Yajun Wang, 2018. "Smooth Trading with Overconfidence and Market Power," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 85(1), pages 611-662.
    10. Marlene Haas & Marius Andrei Zoican, 2016. "Beyond the Frequency Wall: Speed and Liquidity on Batch Auction Markets," Post-Print hal-01484805, HAL.
    11. Andriy Shkilko & Konstantin Sokolov, 2020. "Every Cloud Has a Silver Lining: Fast Trading, Microwave Connectivity, and Trading Costs," Journal of Finance, American Finance Association, vol. 75(6), pages 2899-2927, December.

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    More about this item

    JEL classification:

    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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