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University of Massachusetts


Censored Exploration And The Dark Pool Problem

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.

This talk is based on joint work with Kuzman Ganchev, Michael Kearns, and Yuriy Nevmyvaka.

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