Algorithmic Pricing and Liquidity in Securities Markets

45 Pages Posted: 20 Oct 2022

See all articles by Jean-Edouard Colliard

Jean-Edouard Colliard

HEC Paris - Finance Department

Thierry Foucault

HEC Paris - Finance Department

Stefano Lovo

HEC Paris - Finance Department

Date Written: October 18, 2022

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.

Keywords: Algorithmic pricing, Market Making, Adverse Selection, Market Power, Reinforcement learning

JEL Classification: D43,G10,G14

Suggested Citation

Colliard, Jean-Edouard and Foucault, Thierry and Lovo, Stefano, Algorithmic Pricing and Liquidity in Securities Markets (October 18, 2022). HEC Paris Research Paper, Available at SSRN: https://ssrn.com/abstract=4252858 or http://dx.doi.org/10.2139/ssrn.4252858

Jean-Edouard Colliard (Contact Author)

HEC Paris - Finance Department ( email )

France

Thierry Foucault

HEC Paris - Finance Department ( email )

1 rue de la Liberation
Jouy-en-Josas Cedex, 78351
France
(33)139679569 (Phone)
(33)139677085 (Fax)

HOME PAGE: http://thierryfoucault.com/

Stefano Lovo

HEC Paris - Finance Department ( email )

1 rue de la Liberation
Jouy-en-Josas Cedex, 78351
France

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