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Algorithmic Pricing and Liquidity in Securities Markets

Author

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  • Colliard, Jean-Edouard
  • Foucault, Thierry
  • Lovo, Stefano

Abstract

We let “Algorithmic Market-Makers†(AMMs), using Q-learning algorithms, choose prices for a risky asset when their clients are privately informed about the asset payoff. We find that AMMs learn to cope with adverse selection and to update their prices after observing trades, as predicted by economic theory. However, in contrast to theory, AMMs charge a mark-up over the competitive price, which declines with the number of AMMs. Interestingly, markups tend to decrease with AMMs’ exposure to adverse selection. Accordingly, the sensitivity of quotes to trades is stronger than that predicted by theory and AMMs’ quotes become less competitive over time as asymmetric information declines.

Suggested Citation

  • Colliard, Jean-Edouard & Foucault, Thierry & Lovo, Stefano, 2022. "Algorithmic Pricing and Liquidity in Securities Markets," CEPR Discussion Papers 17606, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:17606
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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

    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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