IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v336y2024i1d10.1007_s10479-022-05120-5.html
   My bibliography  Save this article

A Stackelberg order execution game

Author

Listed:
  • Yinhong Dong

    (Hainan University)

  • Donglei Du

    (University of New Brunswick)

  • Qiaoming Han

    (Nanjing Tech University)

  • Jianfeng Ren

    (Qufu Normal University)

  • Dachuan Xu

    (Beijing Institute for Scientific and Engineering Computing, Beijing University of Technology)

Abstract

Order execution is an important operational level of activity encountered in portfolio investment and risk management. We study a sequential Stackelberg order execution game which arises naturally from the practice of algorithm trading in financial markets. The game consists of two risk-neutral traders, one leader and one follower, who compete to maximize their expected payoffs respectively by trading a single risky asset whose price dynamics follows a linear-price market impact model over a finite horizon. This new Stackelberg game departs from the Nash games which have been the main focus in the algorithm trading literature. We derive a closed-form solution for the unique open-loop Stackelberg equilibrium by exploiting the special structures of the model. This analytic solution enables us to develop new and complementary managerial insights by looking at both players’ equilibrium behavior in terms of trading speeds and positions, expected price dynamics, price of anarchy, first mover’s advantage, and trading horizon effect.

Suggested Citation

  • Yinhong Dong & Donglei Du & Qiaoming Han & Jianfeng Ren & Dachuan Xu, 2024. "A Stackelberg order execution game," Annals of Operations Research, Springer, vol. 336(1), pages 571-604, May.
    • Handle: RePEc:spr:annopr:v:336:y:2024:i:1:d:10.1007_s10479-022-05120-5
    DOI: 10.1007/s10479-022-05120-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-022-05120-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-022-05120-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Liyan Yang & Haoxiang Zhu, 2020. "Back-Running: Seeking and Hiding Fundamental Information in Order Flows," Review of Finance, European Finance Association, vol. 33(4), pages 1484-1533.
    2. Ciamac C. Moallemi & Mehmet Sağlam, 2013. "OR Forum---The Cost of Latency in High-Frequency Trading," Operations Research, INFORMS, vol. 61(5), pages 1070-1086, October.
    3. Holthausen, Robert W. & Leftwich, Richard W. & Mayers, David, 1990. "Large-block transactions, the speed of response, and temporary and permanent stock-price effects," Journal of Financial Economics, Elsevier, vol. 26(1), pages 71-95, July.
    4. I. Muni Toke, 2015. "The order book as a queueing system: average depth and influence of the size of limit orders," Quantitative Finance, Taylor & Francis Journals, vol. 15(5), pages 795-808, May.
    5. Michael Brolley, 2020. "Price Improvement and Execution Risk in Lit and Dark Markets," Management Science, INFORMS, vol. 66(2), pages 863-886, February.
    6. De Long, J Bradford & Andrei Shleifer & Lawrence H. Summers & Robert J. Waldmann, 1990. "Noise Trader Risk in Financial Markets," Journal of Political Economy, University of Chicago Press, vol. 98(4), pages 703-738, August.
    7. Madhavan, Ananth & Cheng, Minder, 1997. "In Search of Liquidity: Block Trades in the Upstairs and Downstairs Markets," The Review of Financial Studies, Society for Financial Studies, vol. 10(1), pages 175-203.
    8. Aurélien Alfonsi & José Infante Acevedo, 2014. "Optimal execution and price manipulations in time-varying limit order books," Post-Print hal-00687193, HAL.
    9. Aurélien Alfonsi & José Infante Acevedo, 2014. "Optimal Execution and Price Manipulations in Time-varying Limit Order Books," Applied Mathematical Finance, Taylor & Francis Journals, vol. 21(3), pages 201-237, July.
    10. Forsyth, P.A. & Kennedy, J.S. & Tse, S.T. & Windcliff, H., 2012. "Optimal trade execution: A mean quadratic variation approach," Journal of Economic Dynamics and Control, Elsevier, vol. 36(12), pages 1971-1991.
    11. Aur'elien Alfonsi & Antje Fruth & Alexander Schied, 2007. "Optimal execution strategies in limit order books with general shape functions," Papers 0708.1756, arXiv.org, revised Feb 2010.
    12. Madhavan, Ananth, 2000. "Market microstructure: A survey," Journal of Financial Markets, Elsevier, vol. 3(3), pages 205-258, August.
    13. Aurelien Alfonsi & Antje Fruth & Alexander Schied, 2010. "Optimal execution strategies in limit order books with general shape functions," Quantitative Finance, Taylor & Francis Journals, vol. 10(2), pages 143-157.
    14. Kraus, Alan & Stoll, Hans R, 1972. "Price Impacts of Block Trading on the New York Stock Exchange," Journal of Finance, American Finance Association, vol. 27(3), pages 569-588, June.
    15. Daniel Mitchell & Jingnan Chen, 2020. "Market or limit orders?," Quantitative Finance, Taylor & Francis Journals, vol. 20(3), pages 447-461, March.
    16. Bruce Ian Carlin & Miguel Sousa Lobo & S. Viswanathan, 2007. "Episodic Liquidity Crises: Cooperative and Predatory Trading," Journal of Finance, American Finance Association, vol. 62(5), pages 2235-2274, October.
    17. Brunovský, Pavol & Černý, Aleš & Komadel, Ján, 2018. "Optimal trade execution under endogenous pressure to liquidate: Theory and numerical solutions," European Journal of Operational Research, Elsevier, vol. 264(3), pages 1159-1171.
    18. Dirk Becherer & Todor Bilarev & Peter Frentrup, 2018. "Optimal liquidation under stochastic liquidity," Finance and Stochastics, Springer, vol. 22(1), pages 39-68, January.
    19. Vayanos, Dimitri, 1998. "Transaction Costs and Asset Prices: A Dynamic Equilibrium Model," The Review of Financial Studies, Society for Financial Studies, vol. 11(1), pages 1-58.
    20. Dan A. Iancu & Nikolaos Trichakis, 2014. "Fairness and Efficiency in Multiportfolio Optimization," Operations Research, INFORMS, vol. 62(6), pages 1285-1301, December.
    21. Alexander Schied & Torsten Schoneborn & Michael Tehranchi, 2010. "Optimal Basket Liquidation for CARA Investors is Deterministic," Applied Mathematical Finance, Taylor & Francis Journals, vol. 17(6), pages 471-489.
    22. David B. Brown & Bruce Ian Carlin & Miguel Sousa Lobo, 2010. "Optimal Portfolio Liquidation with Distress Risk," Management Science, INFORMS, vol. 56(11), pages 1997-2014, November.
    23. Gerry Tsoukalas & Jiang Wang & Kay Giesecke, 2019. "Dynamic Portfolio Execution," Management Science, INFORMS, vol. 67(5), pages 2015-2040, May.
    24. Federico Platania & Pedro Serrano & Mikel Tapia, 2018. "Modelling the shape of the limit order book," Quantitative Finance, Taylor & Francis Journals, vol. 18(9), pages 1575-1597, September.
    25. Alexander Schied & Elias Strehle & Tao Zhang, 2015. "High-frequency limit of Nash equilibria in a market impact game with transient price impact," Papers 1509.08281, arXiv.org, revised May 2017.
    26. Peter Kratz, 2014. "An Explicit Solution of a Nonlinear-Quadratic Constrained Stochastic Control Problem with Jumps: Optimal Liquidation in Dark Pools with Adverse Selection," Mathematics of Operations Research, INFORMS, vol. 39(4), pages 1198-1220, November.
    27. Bertsimas, Dimitris & Lo, Andrew W., 1998. "Optimal control of execution costs," Journal of Financial Markets, Elsevier, vol. 1(1), pages 1-50, April.
    28. Liyan Yang & Haoxiang Zhu, 2020. "Back-Running: Seeking and Hiding Fundamental Information in Order Flows," The Review of Financial Studies, Society for Financial Studies, vol. 33(4), pages 1484-1533.
    29. Beomsoo Park & Benjamin Van Roy, 2015. "Adaptive Execution: Exploration and Learning of Price Impact," Operations Research, INFORMS, vol. 63(5), pages 1058-1076, October.
    30. Alexander Schied & Tao Zhang, 2017. "A State-Constrained Differential Game Arising In Optimal Portfolio Liquidation," Mathematical Finance, Wiley Blackwell, vol. 27(3), pages 779-802, July.
    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. Vayanos, Dimitri & Wang, Jiang, 2013. "Market Liquidity—Theory and Empirical Evidence ," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, volume 2, chapter 0, pages 1289-1361, Elsevier.
    2. Yan, Tingjin & Chiu, Mei Choi & Wong, Hoi Ying, 2023. "Portfolio liquidation with delayed information," Economic Modelling, Elsevier, vol. 126(C).
    3. David B. Brown & Bruce Ian Carlin & Miguel Sousa Lobo, 2010. "Optimal Portfolio Liquidation with Distress Risk," Management Science, INFORMS, vol. 56(11), pages 1997-2014, November.
    4. Vayanos, Dimitri & Wang, Jiang, 2012. "Market liquidity - theory and empirical evidence," LSE Research Online Documents on Economics 119044, London School of Economics and Political Science, LSE Library.
    5. Oehmke, Martin, 2014. "Liquidating illiquid collateral," Journal of Economic Theory, Elsevier, vol. 149(C), pages 183-210.
    6. Kashyap, Ravi, 2020. "David vs Goliath (You against the Markets), A dynamic programming approach to separate the impact and timing of trading costs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    7. Oehmke, Martin, 2014. "Liquidating illiquid collateral," LSE Research Online Documents on Economics 84518, London School of Economics and Political Science, LSE Library.
    8. Julia Ackermann & Thomas Kruse & Mikhail Urusov, 2024. "Self-exciting price impact via negative resilience in stochastic order books," Annals of Operations Research, Springer, vol. 336(1), pages 637-659, May.
    9. Siu, Chi Chung & Guo, Ivan & Zhu, Song-Ping & Elliott, Robert J., 2019. "Optimal execution with regime-switching market resilience," Journal of Economic Dynamics and Control, Elsevier, vol. 101(C), pages 17-40.
    10. Julia Ackermann & Thomas Kruse & Mikhail Urusov, 2021. "Self-exciting price impact via negative resilience in stochastic order books," Papers 2112.03789, arXiv.org, revised Jul 2022.
    11. Francesco Cordoni & Fabrizio Lillo, 2024. "Instabilities in multi-asset and multi-agent market impact games," Annals of Operations Research, Springer, vol. 336(1), pages 505-539, May.
    12. Julia Ackermann & Thomas Kruse & Mikhail Urusov, 2022. "Reducing Obizhaeva-Wang type trade execution problems to LQ stochastic control problems," Papers 2206.03772, arXiv.org, revised Sep 2023.
    13. Lokka, A. & Xu, Junwei, 2020. "Optimal liquidation trajectories for the Almgren-Chriss model," LSE Research Online Documents on Economics 106977, London School of Economics and Political Science, LSE Library.
    14. Olivier Guéant, 2016. "The Financial Mathematics of Market Liquidity: From Optimal Execution to Market Making," Post-Print hal-01393136, HAL.
    15. Phillip Monin, 2014. "Hedging Market Risk in Optimal Liquidation," Working Papers 14-08, Office of Financial Research, US Department of the Treasury.
    16. Alexander Schied & Tao Zhang, 2019. "A Market Impact Game Under Transient Price Impact," Mathematics of Operations Research, INFORMS, vol. 44(1), pages 102-121, February.
    17. Xiangge Luo & Alexander Schied, 2018. "Nash equilibrium for risk-averse investors in a market impact game with transient price impact," Papers 1807.03813, arXiv.org, revised Jun 2019.
    18. Julia Ackermann & Thomas Kruse & Mikhail Urusov, 2021. "Càdlàg semimartingale strategies for optimal trade execution in stochastic order book models," Finance and Stochastics, Springer, vol. 25(4), pages 757-810, October.
    19. Julia Ackermann & Thomas Kruse & Mikhail Urusov, 2020. "C\`adl\`ag semimartingale strategies for optimal trade execution in stochastic order book models," Papers 2006.05863, arXiv.org, revised Jul 2021.
    20. David B. Brown & Bruce Ian Carlin & Miguel Sousa Lobo, 2009. "On the Scholes Liquidation Problem," NBER Working Papers 15381, National Bureau of Economic Research, Inc.

    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:spr:annopr:v:336:y:2024:i:1:d:10.1007_s10479-022-05120-5. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.