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Frequent batch auctions and informed trading

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  • Eibelshäuser, Steffen
  • Smetak, Fabian

Abstract

We study liquidity provision by competitive high-frequency trading firms (HFTs) in a dynamic trading model with private information. Liquidity providers face adverse selection risk from trading with privately informed investors and from trading with other HFTs that engage in latency arbitrage upon public information. The impact of the two different sources of risk depends on the details of the market design. We determine equilibrium transaction costs in continuous limit order book (CLOB) markets and under frequent batch auctions (FBA). In the absence of informed trading, FBA dominates CLOB just as in Budish et al. (2015). Surprisingly, this result does no longer hold with privately informed investors. We show that FBA allows liquidity providers to charge markups and earn profits - even under risk neutrality and perfect competition. A slight variation of the FBA design removes the inefficiency by allowing traders to submit orders conditional on auction excess demand.

Suggested Citation

  • Eibelshäuser, Steffen & Smetak, Fabian, 2022. "Frequent batch auctions and informed trading," SAFE Working Paper Series 344, Leibniz Institute for Financial Research SAFE.
    • Handle: RePEc:zbw:safewp:344
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    References listed on IDEAS

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    1. Varian, Hal R, 1980. "A Model of Sales," American Economic Review, American Economic Association, vol. 70(4), pages 651-659, September.
    2. Lawrence M. Ausubel & Peter Cramton & Marek Pycia & Marzena Rostek & Marek Weretka, 2014. "Demand Reduction and Inefficiency in Multi-Unit Auctions," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(4), pages 1366-1400.
    3. Bellia, Mario & Pelizzon, Loriana & Subrahmanyam, Marti G. & Uno, Jun & Yuferova, Darya, 2017. "Low-latency trading and price discovery: Evidence from the Tokyo Stock Exchange in the pre-opening and opening periods," SAFE Working Paper Series 144, Leibniz Institute for Financial Research SAFE, revised 2017.
    4. 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.
    5. Thierry Foucault & Ailsa Röell & Patrik Sandås, 2003. "Market Making with Costly Monitoring: An Analysis of the SOES Controversy," The Review of Financial Studies, Society for Financial Studies, vol. 16(2), pages 345-384.
    6. Songzi Du & Haoxiang Zhu, 2017. "What is the Optimal Trading Frequency in Financial Markets?," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 84(4), pages 1606-1651.
    7. Copeland, Thomas E & Galai, Dan, 1983. "Information Effects on the Bid-Ask Spread," Journal of Finance, American Finance Association, vol. 38(5), pages 1457-1469, December.
    8. Glosten, Lawrence R. & Milgrom, Paul R., 1985. "Bid, ask and transaction prices in a specialist market with heterogeneously informed traders," Journal of Financial Economics, Elsevier, vol. 14(1), pages 71-100, March.
    9. Albert J. Menkveld & Marius A. Zoican, 2017. "Need for Speed? Exchange Latency and Liquidity," The Review of Financial Studies, Society for Financial Studies, vol. 30(4), pages 1188-1228.
    10. Madhavan, Ananth, 1992. "Trading Mechanisms in Securities Markets," Journal of Finance, American Finance Association, vol. 47(2), pages 607-641, June.
    11. Biais, Bruno & Glosten, Larry & Spatt, Chester, 2005. "Market microstructure: A survey of microfoundations, empirical results, and policy implications," Journal of Financial Markets, Elsevier, vol. 8(2), pages 217-264, May.
    12. Maskin, Eric & Tirole, Jean, 2001. "Markov Perfect Equilibrium: I. Observable Actions," Journal of Economic Theory, Elsevier, vol. 100(2), pages 191-219, October.
    13. Kandel, Eugene & Rindi, Barbara & Bosetti, Luisella, 2012. "The effect of a closing call auction on market quality and trading strategies," Journal of Financial Intermediation, Elsevier, vol. 21(1), pages 23-49.
    14. Kyle, Albert S, 1985. "Continuous Auctions and Insider Trading," Econometrica, Econometric Society, vol. 53(6), pages 1315-1335, November.
    15. Pagano, Michael S. & Peng, Lin & Schwartz, Robert A., 2013. "A call auction's impact on price formation and order routing: Evidence from the NASDAQ stock market," Journal of Financial Markets, Elsevier, vol. 16(2), pages 331-361.
    16. Wah, Elaine & Wellman, Michael P., 2016. "Latency arbitrage in fragmented markets: A strategic agent-based analysis," Algorithmic Finance, IOS Press, vol. 5(3-4), pages 69-93.
    17. Eric M. Aldrich & Kristian López Vargas, 2020. "Experiments in high-frequency trading: comparing two market institutions," Experimental Economics, Springer;Economic Science Association, vol. 23(2), pages 322-352, June.
    18. Markus Baldauf & Joshua Mollner, 2020. "High‐Frequency Trading and Market Performance," Journal of Finance, American Finance Association, vol. 75(3), pages 1495-1526, June.
    19. Daniel Fricke & Austin Gerig, 2018. "Too fast or too slow? Determining the optimal speed of financial markets," Quantitative Finance, Taylor & Francis Journals, vol. 18(4), pages 519-532, April.
    20. Eric Budish & Peter Cramton & John Shim, 2014. "Implementation Details for Frequent Batch Auctions: Slowing Down Markets to the Blink of an Eye," American Economic Review, American Economic Association, vol. 104(5), pages 418-424, May.
    21. Kerry Back & Shmuel Baruch, 2007. "Working Orders in Limit Order Markets and Floor Exchanges," Journal of Finance, American Finance Association, vol. 62(4), pages 1589-1621, August.
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    More about this item

    Keywords

    market design; market microstructure; liquidity provision; high-frequency trading; continuous limit order book; frequent batch auctions; sniping; latency arbitrage;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • D47 - Microeconomics - - Market Structure, Pricing, and Design - - - Market Design

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