IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2508.04344.html
   My bibliography  Save this paper

Performative Market Making

Author

Listed:
  • Charalampos Kleitsikas
  • Stefanos Leonardos
  • Carmine Ventre

Abstract

Financial models do not merely analyse markets, but actively shape them. This effect, known as performativity, describes how financial theories and the subsequent actions based on them influence market processes, by creating self-fulfilling prophecies. Although discussed in the literature on economic sociology, this deeply rooted phenomenon lacks mathematical formulation in financial markets. Our paper closes this gap by breaking down the canonical separation of diffusion processes between the description of the market environment and the financial model. We do that by embedding the model in the process itself, creating a closed feedback loop, and demonstrate how prices change towards greater conformity to the prevailing financial model used in the market. We further show, with closed-form solutions and machine learning, how a performative market maker can reverse engineer the current dominant strategies in the market and effectively arbitrage them while maintaining competitive quotes and superior P&L.

Suggested Citation

  • Charalampos Kleitsikas & Stefanos Leonardos & Carmine Ventre, 2025. "Performative Market Making," Papers 2508.04344, arXiv.org.
    • Handle: RePEc:arx:papers:2508.04344
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2508.04344
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Donald MacKenzie, 2006. "An Engine, Not a Camera: How Financial Models Shape Markets," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262134608, December.
    2. Christopher S Jones & Haitao Mo, 2021. "Out-of-Sample Performance of Mutual Fund Predictors [Has U.S. corporate bond market liquidity deteriorated?]," The Review of Financial Studies, Society for Financial Studies, vol. 34(1), pages 149-193.
    3. Olivier Gu'eant & Charles-Albert Lehalle & Joaquin Fernandez Tapia, 2011. "Dealing with the Inventory Risk. A solution to the market making problem," Papers 1105.3115, arXiv.org, revised Aug 2012.
    4. Thomas Spooner & Rahul Savani, 2020. "Robust Market Making via Adversarial Reinforcement Learning," Papers 2003.01820, arXiv.org, revised Jul 2020.
    5. Pietro Fodra & Mauricio Labadie, 2012. "High-frequency market-making with inventory constraints and directional bets," Papers 1206.4810, arXiv.org.
    6. Ho, Thomas & Stoll, Hans R., 1981. "Optimal dealer pricing under transactions and return uncertainty," Journal of Financial Economics, Elsevier, vol. 9(1), pages 47-73, March.
    7. Christopher S Jones & Haitao Mo & Lauren Cohen, 2021. "Out-of-Sample Performance of Mutual Fund Predictors," Review of Economic Studies, Oxford University Press, vol. 34(1), pages 149-193.
    8. Marco Avellaneda & Sasha Stoikov, 2008. "High-frequency trading in a limit order book," Quantitative Finance, Taylor & Francis Journals, vol. 8(3), pages 217-224.
    9. Pietro Fodra & Mauricio Labadie, 2012. "High-frequency market-making with inventory constraints and directional bets," Working Papers hal-00675925, HAL.
    10. Emile Grunberg & Franco Modigliani, 1954. "The Predictability of Social Events," Journal of Political Economy, University of Chicago Press, vol. 62(6), pages 465-465.
    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. Sofiene El Aoud & Frédéric Abergel, 2015. "A stochastic control approach for options market making," Post-Print hal-01061852, HAL.
    2. Philippe Bergault & David Evangelista & Olivier Gu'eant & Douglas Vieira, 2018. "Closed-form approximations in multi-asset market making," Papers 1810.04383, arXiv.org, revised Sep 2022.
    3. Qing-Qing Yang & Wai-Ki Ching & Jiawen Gu & Tak-Kuen Siu, 2020. "Trading strategy with stochastic volatility in a limit order book market," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 43(1), pages 277-301, June.
    4. Olivier Gu'eant, 2016. "Optimal market making," Papers 1605.01862, arXiv.org, revised May 2017.
    5. Burcu Aydoğan & Ömür Uğur & Ümit Aksoy, 2023. "Optimal Limit Order Book Trading Strategies with Stochastic Volatility in the Underlying Asset," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 289-324, June.
    6. Baron Law & Frederi Viens, 2019. "Market Making under a Weakly Consistent Limit Order Book Model," Papers 1903.07222, arXiv.org, revised Jan 2020.
    7. Diego Zabaljauregui, 2020. "Optimal market making under partial information and numerical methods for impulse control games with applications," Papers 2009.06521, arXiv.org.
    8. Campi, Luciano & Zabaljauregui, Diego, 2020. "Optimal market making under partial information with general intensities," LSE Research Online Documents on Economics 104612, London School of Economics and Political Science, LSE Library.
    9. Thomas Spooner & Rahul Savani, 2020. "Robust Market Making via Adversarial Reinforcement Learning," Papers 2003.01820, arXiv.org, revised Jul 2020.
    10. Ziyi Wang & Carmine Ventre & Maria Polukarov, 2025. "ARL-Based Multi-Action Market Making with Hawkes Processes and Variable Volatility," Papers 2508.16589, arXiv.org.
    11. Nelson Vadori & Leo Ardon & Sumitra Ganesh & Thomas Spooner & Selim Amrouni & Jared Vann & Mengda Xu & Zeyu Zheng & Tucker Balch & Manuela Veloso, 2022. "Towards Multi-Agent Reinforcement Learning driven Over-The-Counter Market Simulations," Papers 2210.07184, arXiv.org, revised Aug 2023.
    12. Joseph Jerome & Leandro Sanchez-Betancourt & Rahul Savani & Martin Herdegen, 2022. "Model-based gym environments for limit order book trading," Papers 2209.07823, arXiv.org.
    13. Pietro Fodra & Huy^en Pham, 2013. "High frequency trading and asymptotics for small risk aversion in a Markov renewal model," Papers 1310.1756, arXiv.org, revised Jan 2015.
    14. Yang Li & Zhi Chen, 2025. "FinFlowRL: An Imitation-Reinforcement Learning Framework for Adaptive Stochastic Control in Finance," Papers 2510.15883, arXiv.org.
    15. Pietro Fodra & Huyen Pham, 2013. "High frequency trading in a Markov renewal model," Working Papers hal-00867113, HAL.
    16. Ziyi Wang & Carmine Ventre & Maria Polukarov, 2025. "Robust Market Making: To Quote, or not To Quote," Papers 2508.16588, arXiv.org.
    17. Marc Hoffmann & Mauricio Labadie & Charles-Albert Lehalle & Gilles Pagès & Huyên Pham & Mathieu Rosenbaum, 2013. "Optimization and statistical methods for high frequency finance," Post-Print hal-01102785, HAL.
    18. Diego Zabaljauregui & Luciano Campi, 2019. "Optimal market making under partial information with general intensities," Papers 1902.01157, arXiv.org, revised Apr 2020.
    19. Pietro Fodra & Mauricio Labadie, 2013. "High-frequency market-making for multi-dimensional Markov processes," Papers 1303.7177, arXiv.org, revised Apr 2013.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:2508.04344. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.