IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i5p1223-d1085829.html
   My bibliography  Save this article

Insights on the Statistics and Market Behavior of Frequent Batch Auctions

Author

Listed:
  • Thiago W. Alves

    (Hanlon Labs, Stevens Institute of Technology, 525 River Street, Babbio Center, Hoboken, NJ 07030, USA
    Interactive Brokers, 2 Pickwick Plaza, Greenwich, CT 06830, USA)

  • Ionuţ Florescu

    (Hanlon Labs, Stevens Institute of Technology, 525 River Street, Babbio Center, Hoboken, NJ 07030, USA
    School of Business, Stevens Institute of Technology, Hoboken, NJ 07310, USA)

  • Dragoş Bozdog

    (Hanlon Labs, Stevens Institute of Technology, 525 River Street, Babbio Center, Hoboken, NJ 07030, USA
    School of Business, Stevens Institute of Technology, Hoboken, NJ 07310, USA)

Abstract

This paper extends previous research performed with the SHIFT financial market simulation platform. In our previous work, we show how this order-driven, distributed asynchronous, and multi-asset simulated environment is capable of reproducing known stylized facts of real continuous double auction financial markets. Using the platform, we study a pricing mechanism based on frequent batch auctions (FBA) proposed by a group of researchers from University of Chicago. We demonstrate our simulator’s capability as an environment to experiment with potential rule changes. We present the first side-by-side comparison of frequent batch auctions with a continuous double auction. We show that FBA is superior in terms of market quality measures but we also discover a potential problem in the technical implementation of FBA.

Suggested Citation

  • Thiago W. Alves & Ionuţ Florescu & Dragoş Bozdog, 2023. "Insights on the Statistics and Market Behavior of Frequent Batch Auctions," Mathematics, MDPI, vol. 11(5), pages 1-26, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:5:p:1223-:d:1085829
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/5/1223/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/5/1223/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Friederich, Sylvain & Payne, Richard, 2015. "Order-to-trade ratios and market liquidity," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 214-223.
    2. Anton Golub & John Keane & Ser-Huang Poon, 2012. "High Frequency Trading and Mini Flash Crashes," Papers 1211.6667, arXiv.org.
    3. Baron, Matthew & Brogaard, Jonathan & Hagströmer, Björn & Kirilenko, Andrei, 2019. "Risk and Return in High-Frequency Trading," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 54(3), pages 993-1024, June.
    4. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    5. Raberto, Marco & Cincotti, Silvano & Focardi, Sergio M. & Marchesi, Michele, 2001. "Agent-based simulation of a financial market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 299(1), pages 319-327.
    6. Lobato, Ignacio N & Velasco, Carlos, 2000. "Long Memory in Stock-Market Trading Volume," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(4), pages 410-427, October.
    7. 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.
    8. Thiago W. Alves & Ionut Florescu & George Calhoun & Dragos Bozdog, 2020. "SHIFT: A Highly Realistic Financial Market Simulation Platform," Papers 2002.11158, arXiv.org, revised Aug 2020.
    9. Jørgensen, Kjell & Skjeltorp, Johannes & Ødegaard, Bernt Arne, 2018. "Throttling hyperactive robots – Order-to-trade ratios at the Oslo Stock Exchange," Journal of Financial Markets, Elsevier, vol. 37(C), pages 1-16.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Thiago W. Alves & Ionut Florescu & George Calhoun & Dragos Bozdog, 2020. "SHIFT: A Highly Realistic Financial Market Simulation Platform," Papers 2002.11158, arXiv.org, revised Aug 2020.
    2. Kemme, David M. & McInish, Thomas H. & Zhang, Jiang, 2022. "Market fairness and efficiency: Evidence from the Tokyo Stock Exchange," Journal of Banking & Finance, Elsevier, vol. 134(C).
    3. Sandrine Jacob Leal & Mauro Napoletano, 2017. "Market Stability vs. Market Resilience: Regulatory Policies Experiments in an Agent-Based Model with Low- and High-Frequency Trading," Post-Print hal-01768876, HAL.
    4. 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).
    5. Loredana Ureche-Rangau & Quiterie de Rorthays, 2009. "More on the volatility-trading volume relationship in emerging markets: The Chinese stock market," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(7), pages 779-799.
    6. Sandrine Jacob Leal & Mauro Napoletano & Andrea Roventini & Giorgio Fagiolo, 2016. "Rock around the clock: An agent-based model of low- and high-frequency trading," Journal of Evolutionary Economics, Springer, vol. 26(1), pages 49-76, March.
    7. Lux, Thomas & Alfarano, Simone, 2016. "Financial power laws: Empirical evidence, models, and mechanisms," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 3-18.
    8. 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.
    9. Khomyn, Marta & Putniņš, Tālis J., 2021. "Algos gone wild: What drives the extreme order cancellation rates in modern markets?," Journal of Banking & Finance, Elsevier, vol. 129(C).
    10. J. Doyne Farmer & John Geanakoplos, 2008. "The virtues and vices of equilibrium and the future of financial economics," Papers 0803.2996, arXiv.org.
    11. Daniel Ladley, 2019. "The Design and Regulation of High Frequency Traders," Discussion Papers in Economics 19/02, Division of Economics, School of Business, University of Leicester.
    12. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.
    13. Alessio Emanuele Biondo, 2018. "Order book microstructure and policies for financial stability," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 35(1), pages 196-218, March.
    14. Leal, Sandrine Jacob & Napoletano, Mauro, 2019. "Market stability vs. market resilience: Regulatory policies experiments in an agent-based model with low- and high-frequency trading," Journal of Economic Behavior & Organization, Elsevier, vol. 157(C), pages 15-41.
    15. Dominique Guegan, 2007. "Global and local stationary modelling in finance: theory and empirical evidence," Post-Print halshs-00187875, HAL.
    16. Troy Tassier, 2013. "Handbook of Research on Complexity, by J. Barkley Rosser, Jr. and Edward Elgar," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 39(1), pages 132-133.
    17. Segnon, Mawuli & Lux, Thomas, 2013. "Multifractal models in finance: Their origin, properties, and applications," Kiel Working Papers 1860, Kiel Institute for the World Economy (IfW Kiel).
    18. Nidhi Aggarwal & Venkatesh Panchapagesan & Susan Thomas, 2019. "When do regulatory interventions work?," Working Papers id:13040, eSocialSciences.
    19. Biondo, Alessio Emanuele, 2017. "Learning to forecast, risk aversion, and microstructural aspects of financial stability," Economics Discussion Papers 2017-104, Kiel Institute for the World Economy (IfW Kiel).
    20. Sandra Ferreruela & Daniel Martín, 2022. "Market Quality and Short-Selling Ban during the COVID-19 Pandemic: A High-Frequency Data Approach," JRFM, MDPI, vol. 15(7), pages 1-29, July.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:5:p:1223-:d:1085829. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.