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Order Protection through Delayed Messaging

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  • Aldrich, Eric M
  • Friedman, Daniel

Abstract

Several financial exchanges have recently introduced messaging delays (e.g., a 350 microsecond delay at IEX and NYSE American) intended to protect ordinary investors from high-frequency traders who exploit stale orders. We propose an equilibrium model of this exchange design as a modification of the standard continuous double auction market format. The model predicts that a messaging delay will generally improve price efficiency and lower transactions cost but will increase queuing costs. Some of the predictions are testable in the field or in a laboratory environment.
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Suggested Citation

  • Aldrich, Eric M & Friedman, Daniel, 2019. "Order Protection through Delayed Messaging," Santa Cruz Department of Economics, Working Paper Series qt4938f518, Department of Economics, UC Santa Cruz.
  • Handle: RePEc:cdl:ucscec:qt4938f518
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    References listed on IDEAS

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    1. Degryse, Hans & Van Achter, Mark & Wuyts, Gunther, 2009. "Dynamic order submission strategies with competition between a dealer market and a crossing network," Journal of Financial Economics, Elsevier, vol. 91(3), pages 319-338, March.
    2. Foucault, Thierry, 1998. "Order Flow Composition and Trading Costs in Dynamic Limit Order Markets," CEPR Discussion Papers 1817, C.E.P.R. Discussion Papers.
    3. Breckenfelder, Johannes, 2013. "Competition between high-frequency traders, and market quality," MPRA Paper 66715, University Library of Munich, Germany, revised Dec 2013.
    4. 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.
    5. Michael Goldstein & Jonathan Brogaard & Terrence Hendershott & Stefan Hunt & Carla Ysusi, 2014. "High-Frequency Trading and the Execution Costs of Institutional Investors," The Financial Review, Eastern Finance Association, vol. 49(2), pages 345-369, May.
    6. Michael Goldstein & Björn Hagströmer & Lars Nordén & Dong Zhang, 2014. "How Aggressive Are High-Frequency Traders?," The Financial Review, Eastern Finance Association, vol. 49(2), pages 395-419, May.
    7. 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.
    8. Brolley, Michael & Cimon, David A., 2020. "Order-Flow Segmentation, Liquidity, and Price Discovery: The Role of Latency Delays," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 55(8), pages 2555-2587, December.
    9. Albert S Kyle & Jeongmin Lee, 2017. "Toward a fully continuous exchange," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 33(4), pages 650-675.
    10. Haoxiang Zhu, 2014. "Do Dark Pools Harm Price Discovery?," The Review of Financial Studies, Society for Financial Studies, vol. 27(3), pages 747-789.
    11. Jonathan Brogaard & Terrence Hendershott & Ryan Riordan, 2019. "Price Discovery without Trading: Evidence from Limit Orders," Journal of Finance, American Finance Association, vol. 74(4), pages 1621-1658, August.
    12. David Easley & Marcos M. López de Prado & Maureen O'Hara, 2012. "Flow Toxicity and Liquidity in a High-frequency World," The Review of Financial Studies, Society for Financial Studies, vol. 25(5), pages 1457-1493.
    13. Khapko, Mariana & Zoican, Marius, 2021. "Do speed bumps curb low-latency investment? Evidence from a laboratory market," Journal of Financial Markets, Elsevier, vol. 55(C).
    14. Brogaard, Jonathan & Garriott, Corey, 2019. "High-Frequency Trading Competition," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 54(4), pages 1469-1497, August.
    15. 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.
    16. Foucault, Thierry, 1999. "Order flow composition and trading costs in a dynamic limit order market1," Journal of Financial Markets, Elsevier, vol. 2(2), pages 99-134, May.
    17. Werner, Ingrid M. & Wen, Yuanji & Rindi, Barbara & Consonni, Francesco & Buti, Sabrina, 2015. "Tick Size: Theory and Evidence," Working Paper Series 2015-04, Ohio State University, Charles A. Dice Center for Research in Financial Economics.
    18. 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.
    19. Jonathan Brogaard & Björn Hagströmer & Lars Nordén & Ryan Riordan, 2015. "Trading Fast and Slow: Colocation and Liquidity," The Review of Financial Studies, Society for Financial Studies, vol. 28(12), pages 3407-3443.
    20. 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.
    21. Terrence Hendershott & Haim Mendelson, 2000. "Crossing Networks and Dealer Markets: Competition and Performance," Journal of Finance, American Finance Association, vol. 55(5), pages 2071-2115, October.
    22. Ingrid M. Werner & Barbara Rindi & Sabrina Buti & Yuanji Wen, 2022. "Tick Size, Trading Strategies and Market Quality," Post-Print hal-03591205, HAL.
    23. Hoffmann, Peter, 2014. "A dynamic limit order market with fast and slow traders," Journal of Financial Economics, Elsevier, vol. 113(1), pages 156-169.
    24. Eric Budish & Peter Cramton & John Shim, 2015. "Editor's Choice The High-Frequency Trading Arms Race: Frequent Batch Auctions as a Market Design Response," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 130(4), pages 1547-1621.
    25. Markus Baldauf & Joshua Mollner, 2020. "High‐Frequency Trading and Market Performance," Journal of Finance, American Finance Association, vol. 75(3), pages 1495-1526, June.
    26. Tomasz Kozubowski & Seidu Inusah, 2006. "A Skew Laplace Distribution on Integers," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 58(3), pages 555-571, September.
    27. Buti, Sabrina & Rindi, Barbara & Werner, Ingrid M., 2017. "Dark pool trading strategies, market quality and welfare," Journal of Financial Economics, Elsevier, vol. 124(2), pages 244-265.
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    Cited by:

    1. Collins, Sean M. & James, Duncan & Servátka, Maroš & Vadovič, Radovan, 2021. "Attainment of equilibrium via Marshallian path adjustment: Queueing and buyer determinism," Games and Economic Behavior, Elsevier, vol. 125(C), pages 94-106.
    2. Angerer, Martin & Neugebauer, Tibor & Shachat, Jason, 2023. "Arbitrage bots in experimental asset markets," Journal of Economic Behavior & Organization, Elsevier, vol. 206(C), pages 262-278.
    3. Kyungsub Lee & Byoung Ki Seo, 2022. "Modeling bid and ask price dynamics with an extended Hawkes process and its empirical applications for high-frequency stock market data," Papers 2201.10173, arXiv.org.
    4. Mariana Khapko & Marius Zoican, 2019. "Do speed bumps curb low-latency trading? Evidence from a laboratory market," Papers 1910.03068, arXiv.org.
    5. 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.

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    More about this item

    Keywords

    Market design; high-frequency trading; continuous double auction; IEX; lab experiments;
    All these keywords.

    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
    • D47 - Microeconomics - - Market Structure, Pricing, and Design - - - Market Design
    • D53 - Microeconomics - - General Equilibrium and Disequilibrium - - - Financial Markets
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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