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Algorithmic Pricing and Liquidity in Securities Markets

Author

Listed:
  • Colliard, Jean-Edouard

    (HEC Paris)

  • Foucault, Thierry

    (HEC Paris)

  • Lovo, Stefano

    (HEC Paris)

Abstract

We let ``Algorithmic Market Makers'' (AMs), using Q-learning algorithms, determine prices for a risky asset in a standard market making game with adverse selection and compare these prices to the Nash equilibrium of the game. We observe that AMs effectively adapt to adverse selection, adjusting prices post-trade as anticipated. However, AMs charge a markup over the competitive price and this markup increases when adverse selection costs decrease, in contrast to the predictions of the Nash equilibrium. We attribute this unexpected pattern to the diminished learning capacity of AMs when faced with increased profit variance.

Suggested Citation

  • Colliard, Jean-Edouard & Foucault, Thierry & Lovo, Stefano, 2022. "Algorithmic Pricing and Liquidity in Securities Markets," HEC Research Papers Series 1459, HEC Paris.
    • Handle: RePEc:ebg:heccah:1459
    DOI: 10.2139/ssrn.4252858
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    Cited by:

    1. Umut Cetin & Kasper Larsen, 2023. "Is Kyle's equilibrium model stable?," Papers 2307.09392, arXiv.org, revised Jul 2023.

    More about this item

    Keywords

    Algorithmic pricing; Market Making; Adverse Selection; Market Power; Reinforcement learning;
    All these keywords.

    JEL classification:

    • D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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