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https://us06web.zoom.us/j/82768721086?pwd=rTeG8REIjkuMdsclRZZ4oWkb4YmQJe.1
Meeting ID: 827 6872 1086
Passcode: 233290
Abstract: Optimal execution of large orders is a classical problem in mathematical finance and of great importance for the industry. Most models make simplifying assumptions in order to achieve analytical tractability. With an alternative approach, I will present some recent advancements obtained with Reinforcement Learning (RL) to cases when liquidity is time varying and when multiple executions are present at the same time. Specifically, I will first show that when market impact is time varying, RL based techniques are able to find solutions which are superior to approximated analytical solutions. Then, I will consider the case of multiple RL agents simultaneously performing an optimal execution and I will show that the strategies learned by the agents deviate significantly from the Nash equilibrium of the corresponding market impact game, suggesting that the learned strategies exhibit tacit collusion.
Bio: Fabrizio Lillo is Full Professor of Mathematical Methods for Economics and Finance at the University of Bologna and since July 2020 he also holds the chair in Financial Mathematics at the Scuola Normale Superiore in Pisa on the project "Microstructure of Financial Markets - Networks and Systemic Risk". Formerly he was Associate Professor at the Scuola Normale Superiore, leading the Quantitative Finance group from 2011 to 2017. He has also been a postdoc, External Faculty and Professor at the Santa Fe Institute (USA). He received the PhD in Physics and was assistant professor at the University of Palermo (Italy). He has been awarded the Young Scientist Award for Socio- and Econophysics of the German Physical Society in 2007. He is the author of 140 referred scientific papers, which, according to Google Scholar, have received more than 12,000 citations. He is also a member of the editorial board of several scientific journals. His research interests lie at the intersection between mathematics, statistics, and computer science mostly focusing on social, economic, and financial systems.
