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Reinforcement Learning Equilibrium in Limit Order Markets

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  • He, Xue-Zhong
  • Lin, Shen

Abstract

This paper introduces an information-based reinforcement learning to exploit information channels to traders’ trading behavior in an equilibrium limit order market. Anticipating that informed traders are more likely to submit market buy (sell) orders when asset is significantly under (over) valued, uninformed traders tend to chase market buy (sell) orders of the informed to buy (sell). To gain from the order chasing of the uninformed, informed traders strategically submit more market buy (sell) and limit sell (buy) orders. This amplifies the order chasing behaviour of the uninformed, generating predictable trading behaviours that can improve information efficiency but reduce market liquidity. Order book information and learning can have opposite effects on order choices and endogenous liquidity provision for the informed and uninformed. Furthermore, more informed trading is beneficial, but fast trading can be harmful for market quality.

Suggested Citation

  • He, Xue-Zhong & Lin, Shen, 2022. "Reinforcement Learning Equilibrium in Limit Order Markets," Journal of Economic Dynamics and Control, Elsevier, vol. 144(C).
  • Handle: RePEc:eee:dyncon:v:144:y:2022:i:c:s0165188922002019
    DOI: 10.1016/j.jedc.2022.104497
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    References listed on IDEAS

    as
    1. Roberto Dieci & Xue-Zhong He, 2018. "Heterogeneous Agent Models in Finance," Research Paper Series 389, Quantitative Finance Research Centre, University of Technology, Sydney.
    2. Sebastien Pouget, 2007. "Adaptive Traders and the Design of Financial Markets," Journal of Finance, American Finance Association, vol. 62(6), pages 2835-2863, December.
    3. Chiarella, Carl & Iori, Giulia, 2009. "The impact of heterogeneous trading rules on the limit order book and order flows," Journal of Economic Dynamics and Control, Elsevier, vol. 33(3), pages 525-537.
    4. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-881, September.
    5. Biais, Bruno & Foucault, Thierry & Moinas, Sophie, 2015. "Equilibrium fast trading," Journal of Financial Economics, Elsevier, vol. 116(2), pages 292-313.
    6. Ladley, Daniel, 2020. "The high frequency trade off between speed and sophistication," Journal of Economic Dynamics and Control, Elsevier, vol. 116(C).
    7. Elise Payzan-LeNestour & Peter Bossaerts, 2015. "Learning About Unstable, Publicly Unobservable Payoffs," Review of Financial Studies, Society for Financial Studies, vol. 28(7), pages 1874-1913.
    8. Markku Kaustia & Samuli Knüpfer, 2008. "Do Investors Overweight Personal Experience? Evidence from IPO Subscriptions," Journal of Finance, American Finance Association, vol. 63(6), pages 2679-2702, December.
    9. Arthur, W.B. & Holland, J.H. & LeBaron, B. & Palmer, R. & Tayler, P., 1996. "Asset Pricing Under Endogenous Expectations in an Artificial Stock Market," Working papers 9625, Wisconsin Madison - Social Systems.
    10. Biais, Bruno & Hillion, Pierre & Spatt, Chester, 1995. "An Empirical Analysis of the Limit Order Book and the Order Flow in the Paris Bourse," Journal of Finance, American Finance Association, vol. 50(5), pages 1655-1689, December.
    11. Thierry Foucault & Johan Hombert & Ioanid Roşu, 2016. "News Trading and Speed," Journal of Finance, American Finance Association, vol. 71(1), pages 335-382, February.
    12. Yeh, Chia-Hsuan & Yang, Chun-Yi, 2010. "Examining the effectiveness of price limits in an artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 34(10), pages 2089-2108, October.
    13. David Easley & Marcos López de Prado & Maureen O’Hara & Zhibai Zhang & Wei Jiang, 2021. "Microstructure in the Machine Age [The risk of machine learning]," Review of Financial Studies, Society for Financial Studies, vol. 34(7), pages 3316-3363.
    14. Bongaerts, Dion & Achter, Mark Van, 2021. "Competition among liquidity providers with access to high-frequency trading technology," Journal of Financial Economics, Elsevier, vol. 140(1), pages 220-249.
    15. Theissen, Erik, 2000. "Market structure, informational efficiency and liquidity: An experimental comparison of auction and dealer markets," Journal of Financial Markets, Elsevier, vol. 3(4), pages 333-363, November.
    16. James J. Choi & David Laibson & Brigitte C. Madrian & Andrew Metrick, 2009. "Reinforcement Learning and Savings Behavior," Journal of Finance, American Finance Association, vol. 64(6), pages 2515-2534, December.
    17. Carl Chiarella & Giulia Iori, 2002. "A simulation analysis of the microstructure of double auction markets," Quantitative Finance, Taylor & Francis Journals, vol. 2(5), pages 346-353.
    18. LeBaron, Blake, 2006. "Agent-based Computational Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 24, pages 1187-1233, Elsevier.
    19. Goettler, Ronald L. & Parlour, Christine A. & Rajan, Uday, 2009. "Informed traders and limit order markets," Journal of Financial Economics, Elsevier, vol. 93(1), pages 67-87, July.
    20. Lensberg, Terje & Schenk-Hoppé, Klaus Reiner & Ladley, Dan, 2015. "Costs and benefits of financial regulation: Short-selling bans and transaction taxes," Journal of Banking & Finance, Elsevier, vol. 51(C), pages 103-118.
    21. LeBaron, Blake & Arthur, W. Brian & Palmer, Richard, 1999. "Time series properties of an artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 23(9-10), pages 1487-1516, September.
    22. Hommes, Cars H., 2006. "Heterogeneous Agent Models in Economics and Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 23, pages 1109-1186, Elsevier.
    23. David Easley & Marcos López de Prado & Maureen O’Hara & Zhibai Zhang, 2021. "Microstructure in the Machine Age," NBER Chapters, in: Big Data: Long-Term Implications for Financial Markets and Firms, pages 3316-3363, National Bureau of Economic Research, Inc.
    24. Shu-Heng Chen & Yi-Lin Hsieh, 2011. "Reinforcement Learning in Experimental Asset Markets," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 37(1), pages 109-133.
    25. Breckenfelder, Johannes, 2013. "Competition between high-frequency traders, and market quality," MPRA Paper 66715, University Library of Munich, Germany, revised Dec 2013.
    26. James J. Choi & David Laibson & Brigitte C. Madrian & Andrew Metrick, 2009. "Reinforcement Learning and Savings Behavior," Journal of Finance, American Finance Association, vol. 64(6), pages 2515-2534, December.
    27. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    28. Chiarella, Carl & He, Xue-Zhong & Pellizzari, Paolo, 2012. "A Dynamic Analysis Of The Microstructure Of Moving Average Rules In A Double Auction Market," Macroeconomic Dynamics, Cambridge University Press, vol. 16(4), pages 556-575, September.
    29. Albert J. Menkveld, 2016. "The Economics of High-Frequency Trading: Taking Stock," Annual Review of Financial Economics, Annual Reviews, vol. 8(1), pages 1-24, October.
    30. Duffy, John, 2006. "Agent-Based Models and Human Subject Experiments," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 19, pages 949-1011, Elsevier.
    31. Chakravarty Sugato & Holden Craig W., 1995. "An Integrated Model of Market and Limit Orders," Journal of Financial Intermediation, Elsevier, vol. 4(3), pages 213-241, July.
    32. Ronald L. Goettler & Christine A. Parlour & Uday Rajan, 2005. "Equilibrium in a Dynamic Limit Order Market," Journal of Finance, American Finance Association, vol. 60(5), pages 2149-2192, October.
    33. 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.
    34. Chiarella, Carl & He, Xue-Zhong & Wei, Lijian, 2015. "Learning, information processing and order submission in limit order markets," Journal of Economic Dynamics and Control, Elsevier, vol. 61(C), pages 245-268.
    35. Arifovic, Jasmina & He, Xue-zhong & Wei, Lijian, 2022. "Machine learning and speed in high-frequency trading," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).
    36. Arifovic, Jasmina, 1996. "The Behavior of the Exchange Rate in the Genetic Algorithm and Experimental Economies," Journal of Political Economy, University of Chicago Press, vol. 104(3), pages 510-541, June.
    37. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    38. Hoffmann, Peter, 2014. "A dynamic limit order market with fast and slow traders," Journal of Financial Economics, Elsevier, vol. 113(1), pages 156-169.
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    More about this item

    Keywords

    Limit order market; reinforcement learning; order chasing; endogenous liquidity provision; and price discovery;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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