There has been much work over the last two decades on how to trade in an optimal fashion on electronic markets. In this talk, I will provide an overview of several aspects of this class of problems ranging over their formulations as stochastic control problems, to their generalization to the many player setting where they may be approximated as mean field games, and finally how model-free reinforcement learning may play a role in numerically solving previously intractable problems.
Market impact is one of the most important sources of trading cost for financial investors executing large orders. Nonetheless its measurement, modeling, and control are still not fully understood. I will present some recent advances on this topic, considering both empirical aspects, both in univariate and in portfolio setting, and modeling approaches, considering both reduced form modesl and approaches describing explicitly the limit order book dynamics. Finally, I will discuss the relevance of these findings for the problem of optimal execution of large trades. Slides.
We present new convergence results for the so-called stochastic gradient Langevin algorithm. Price prediction is a potential application. We also explain some new research directions involving higher order regularization. Slides.
The workshop is free for registered participants. You can register until Nov 19, 2021. If you want to cancel your registration contact the organizers at riskconf@ttk.elte.hu.
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email: riskconf@ttk.elte.hu
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