Algorithmic Pricing and Liquidity in Securities Markets

Jean-Edouard Colliard, Thierry Foucault and Stefano Lovo
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Stefano Lovo: HEC Paris

No 1459, HEC Research Papers Series from 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.

Keywords: Algorithmic pricing; Market Making; Adverse Selection; Market Power; Reinforcement learning (search for similar items in EconPapers)
JEL-codes: D43 G10 G14 (search for similar items in EconPapers)
Pages: 70 pages
Date: 2022-10-20
New Economics Papers: this item is included in nep-ain, nep-com, nep-gth and nep-mst
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Related works:
Working Paper: Algorithmic Pricing and Liquidity in Securities Markets (2022)
Working Paper: Algorithmic Pricing and Liquidity in Securities Markets (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:ebg:heccah:1459

DOI: 10.2139/ssrn.4252858

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