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.

Bio：
Jean-Edouard Colliard is Associate Professor of Finance at HEC Paris, which he joined in 2014. He obtained a PhD in Economics in 2012 from the Paris School of Economics. Before joining HEC, Jean-Edouard worked for two years as an economist in the Research department of the European Central Bank. He is a co-holder of the research chair "Analytics for Future Banking" (HEC Paris - Natixis - Polytechnique). 

His main research areas are the regulation of financial institutions and the microstructure of financial markets, including topics such as financial transactions taxes, over-the counter markets, bank capital requirements, or the European Banking Union. Jean-Edouard's research has been published in leading finance and managmeent journals such as the Journal of Finance, the Review of Financial Studies, the Review of Finance, and Management Science. He is an Associate Editor of the journal Management Science.

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