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Cooperation between Independent Market Makers

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  • Bingyan Han

Abstract

With the digitalization of the financial market, dealers are increasingly handling market-making activities by algorithms. Recent antitrust literature raises concerns on collusion caused by artificial intelligence. This paper studies the possibility of cooperation between market makers via independent Q-learning. Market making with inventory risk is formulated as a repeated general-sum game. Under a stag-hunt type payoff, we find that market makers can learn cooperative strategies without communication. In general, high spreads can have the largest probability even when the lowest spread is the unique Nash equilibrium. Moreover, introducing more agents into the game does not necessarily eliminate the presence of supra-competitive spreads.

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  • Bingyan Han, 2022. "Cooperation between Independent Market Makers," Papers 2206.05410, arXiv.org.
    • Handle: RePEc:arx:papers:2206.05410
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    References listed on IDEAS

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    1. Christie, William G & Harris, Jeffrey H & Schultz, Paul H, 1994. "Why Did NASDAQ Market Makers Stop Avoiding Odd-Eighth Quotes?," Journal of Finance, American Finance Association, vol. 49(5), pages 1841-1860, December.
    2. Christie, William G & Schultz, Paul H, 1994. "Why Do NASDAQ Market Makers Avoid Odd-Eighth Quotes?," Journal of Finance, American Finance Association, vol. 49(5), pages 1813-1840, December.
    3. Sergiu Hart & Andreu Mas-Colell, 2013. "A Simple Adaptive Procedure Leading To Correlated Equilibrium," World Scientific Book Chapters, in: Simple Adaptive Strategies From Regret-Matching to Uncoupled Dynamics, chapter 2, pages 17-46, World Scientific Publishing Co. Pte. Ltd..
    4. Emilio Calvano & Giacomo Calzolari & Vincenzo Denicolò & Sergio Pastorello, 2020. "Artificial Intelligence, Algorithmic Pricing, and Collusion," American Economic Review, American Economic Association, vol. 110(10), pages 3267-3297, October.
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    6. Waltman, Ludo & Kaymak, Uzay, 2008. "Q-learning agents in a Cournot oligopoly model," Journal of Economic Dynamics and Control, Elsevier, vol. 32(10), pages 3275-3293, October.
    7. Sumitra Ganesh & Nelson Vadori & Mengda Xu & Hua Zheng & Prashant Reddy & Manuela Veloso, 2019. "Reinforcement Learning for Market Making in a Multi-agent Dealer Market," Papers 1911.05892, arXiv.org.
    8. Bastien Baldacci & Dylan Possamai & Mathieu Rosenbaum, 2019. "Optimal make take fees in a multi market maker environment," Papers 1907.11053, arXiv.org, revised Mar 2021.
    9. William G. Christie & Paul H. Schultz, 1995. "Policy Watch: Did Nasdaq Market Makers Implicitly Collude?," Journal of Economic Perspectives, American Economic Association, vol. 9(3), pages 199-208, Summer.
    10. Pai, Mallesh & Hansen, Karsten, 2020. "Algorithmic Collusion: Supra-competitive Prices via Independent Algorithms," CEPR Discussion Papers 14372, C.E.P.R. Discussion Papers.
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    Cited by:

    1. Brown, David P. & Eckert, Andrew & Silveira, Douglas, 2023. "Screening for collusion in wholesale electricity markets: A literature review," Utilities Policy, Elsevier, vol. 85(C).

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