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Prof. Michael Kearns on Reinforcement Learning Approaches to Algorithmic Trading
Michael Kearns will discuss the use of reinforcement learning methods from machine learning for problems in algorithmic trading. Reinforcement learning takes a state-based, control-theoretic approach that explicitly considers the trade-off between exploration and exploitation during the learning process.
Prof. Kearns will present two case studies along these lines. The first is a large-scale empirical study of the application of reinforcement learning to the problem of optimized trade execution. The second is an algorithmic and theoretical examination of the problem of smart order routing in dark pools.
See http://www.thalesians.com/finance/index.php/Events/Seminars/Seminar76 for further details.
Michael Kearns will discuss the use of reinforcement learning methods from machine learning for problems in algorithmic trading. Reinforcement learning takes a state-based, control-theoretic approach that explicitly considers the trade-off between exploration and exploitation during the learning process.
Prof. Kearns will present two case studies along these lines. The first is a large-scale empirical study of the application of reinforcement learning to the problem of optimized trade execution. The second is an algorithmic and theoretical examination of the problem of smart order routing in dark pools.
See http://www.thalesians.com/finance/index.php/Events/Seminars/Seminar76 for further details.