Censored Exploration and the Dark Pool Problem

Kuzman Ganchev, Michael Kearns, Yuriy Nevmyvaka, Jennifer Wortman
Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, PMLR R7:193-202, 2009.

Abstract

Dark pools are a 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 dark pool execution data from a large brokerage.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR7-ganchev09a, title = {Censored Exploration and the Dark Pool Problem}, author = {Ganchev, Kuzman and Kearns, Michael and Nevmyvaka, Yuriy and Wortman, Jennifer}, booktitle = {Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence}, pages = {193--202}, year = {2009}, editor = {Bilmes, Jeff and Ng, Andrew Y.}, volume = {R7}, series = {Proceedings of Machine Learning Research}, month = {18--21 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r7/main/assets/ganchev09a/ganchev09a.pdf}, url = {https://proceedings.mlr.press/r7/ganchev09a.html}, abstract = {Dark pools are a 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 dark pool execution data from a large brokerage.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Censored Exploration and the Dark Pool Problem %A Kuzman Ganchev %A Michael Kearns %A Yuriy Nevmyvaka %A Jennifer Wortman %B Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2009 %E Jeff Bilmes %E Andrew Y. Ng %F pmlr-vR7-ganchev09a %I PMLR %P 193--202 %U https://proceedings.mlr.press/r7/ganchev09a.html %V R7 %X Dark pools are a 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 dark pool execution data from a large brokerage. %Z Reissued by PMLR on 04 October 2026.
APA
Ganchev, K., Kearns, M., Nevmyvaka, Y. & Wortman, J.. (2009). Censored Exploration and the Dark Pool Problem. Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R7:193-202 Available from https://proceedings.mlr.press/r7/ganchev09a.html. Reissued by PMLR on 04 October 2026.

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