Seminar
"Censored Exploration and the Dark Pool Problem"
Jennifer Wortman Vaughan, Harvard University
Friday, November 6, 2009, at 12:00 Noon
Room 368 (CIT 3rd floor)
Dark pools are a relatively recent type of stock exchange in which information about outstanding orders is deliberately hidden in order to minimize the market impact of large-volume trades. The success and proliferation of dark pools have created challenging and interesting problems in algorithmic trading --- in particular, the problem of optimizing the allocation of a large trade over multiple competing dark pools. In this work we formalize this optimization as a problem of multi-venue exploration from censored data, and provide a provably efficient and near-optimal algorithm for its solution. Our algorithm and its analysis have much in common with well-studied algorithms for managing the exploration-exploitation trade-off in reinforcement learning. We also provide an extensive experimental evaluation of our algorithm using real trading data.
Bio:
Jenn Wortman Vaughan is a postdoctoral research fellow at Harvard University. She completed her Ph.D. at the University of Pennsylvania in 2009. Her research interests are in machine learning, computational economics, social network theory, and algorithms, all of which she studies using techniques from theoretical computer science. Her recent research has won several best student paper awards, as well as Penn's 2009 Rubinoff dissertation award for innovative applications of computer technology. In her spare time, she is involved in a variety of efforts to provide support for women in computer science; most notably, she co-founded the Annual Workshop for Women in Machine Learning, which will be held for the fourth time this year. Next year Jenn will join the Computer Science Department at UCLA as an assistant professor.
This talk is based on joint work with Kuzman Ganchev, Michael Kearns, and Yuriy Nevmyvaka.
Host: Amy Greenwald
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