Censored Exploration in Dark Pools

Michael Kearns
University of Pennsylvania


Wednesday, October 21, 2009
4:30 - 5:30 PM
Packard, 202

Abstract:

Dark pools are a relatively recent type of equities exchange in which transparency is deliberately limited in order to minimize the market impact of large-volume trades. The success and proliferation of dark pools has also led to a challenging and interesting problem in algorithmic trading --- namely, optimizing the distribution of a large trade over multiple competing dark pools. In this work we formalize this as a problem of multi-venue exploration from censored data, and provide a provably efficient and near-optimal algorithm for its solution. This algorithm and its analysis has much in common with well-studied algorithms for exploration-exploitation in reinforcement learning, and is evaluated on dark pool execution data from a large brokerage.
Joint work with Kuzman Ganchev, Yuriy Nevmyvaka, and Jennifer Wortman Vaughan.

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