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
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.
Michael Kearns is a professor in the Computer and Information Science department at the University of Pennsylvania, where he holds the National Center Chair and has joint appointments in the Wharton School. He is founding director of Penn's new Market and Social Systems Engineering (MKSE) program (www.mkse.upenn.edu). His research interests include topics in machine learning, computational finance, social networks, and algorithmic game theory. He has consulted extensively in the technology and finance industries, and is currently a co-PM in the MultiQuant division of SAC Capital in New York.
Further information is available at www.cis.upenn.edu/~mkearns.
ABOUT THE SERIES
The IAFE's Thalesians Seminar Series is a joint effort on the part of the IAFE (www.iafe.org) and the Thalesians (www.thalesians.com). The goal of the series is to provide a forum for the exchange of new ideas and results related to the field of quantitative finance. This goal is accomplished by hosting seminars where leading practitioners and academics present new work, and following the seminars with a reception to facilitate further interaction and discussion. Click here for information on the IAFE/Thalesian Seminar Series.