Optimal Allocation Strategies for the Dark Pool Problem

Alekh Agarwal, Peter Bartlett, Max Dama
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:9-16, 2010.

Abstract

We study the problem of allocating stocks to dark pools. We propose and analyze an optimal approach for allocations, if continuous-valued allocations are allowed. We also propose a modification for the case when only integer-valued allocations are possible. We extend the previous work on this problem (Ganchev et al., 2009) to adversarial scenarios, while also improving over their results in the iid setup. The resulting algorithms are efficient, and perform well in simulations under stochastic and adversarial inputs.

Cite this Paper

BibTeX
@InProceedings{pmlr-v9-agarwal10a, title = {Optimal Allocation Strategies for the Dark Pool Problem}, author = {Agarwal, Alekh and Bartlett, Peter and Dama, Max}, booktitle = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics}, pages = {9--16}, year = {2010}, editor = {Teh, Yee Whye and Titterington, Mike}, volume = {9}, series = {Proceedings of Machine Learning Research}, address = {Chia Laguna Resort, Sardinia, Italy}, month = {13--15 May}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v9/agarwal10a/agarwal10a.pdf}, url = {https://proceedings.mlr.press/v9/agarwal10a.html}, abstract = {We study the problem of allocating stocks to dark pools. We propose and analyze an optimal approach for allocations, if continuous-valued allocations are allowed. We also propose a modification for the case when only integer-valued allocations are possible. We extend the previous work on this problem (Ganchev et al., 2009) to adversarial scenarios, while also improving over their results in the iid setup. The resulting algorithms are efficient, and perform well in simulations under stochastic and adversarial inputs.} }
Endnote
%0 Conference Paper %T Optimal Allocation Strategies for the Dark Pool Problem %A Alekh Agarwal %A Peter Bartlett %A Max Dama %B Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2010 %E Yee Whye Teh %E Mike Titterington %F pmlr-v9-agarwal10a %I PMLR %P 9--16 %U https://proceedings.mlr.press/v9/agarwal10a.html %V 9 %X We study the problem of allocating stocks to dark pools. We propose and analyze an optimal approach for allocations, if continuous-valued allocations are allowed. We also propose a modification for the case when only integer-valued allocations are possible. We extend the previous work on this problem (Ganchev et al., 2009) to adversarial scenarios, while also improving over their results in the iid setup. The resulting algorithms are efficient, and perform well in simulations under stochastic and adversarial inputs.
RIS
TY - CPAPER TI - Optimal Allocation Strategies for the Dark Pool Problem AU - Alekh Agarwal AU - Peter Bartlett AU - Max Dama BT - Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics DA - 2010/03/31 ED - Yee Whye Teh ED - Mike Titterington ID - pmlr-v9-agarwal10a PB - PMLR DP - Proceedings of Machine Learning Research VL - 9 SP - 9 EP - 16 L1 - http://proceedings.mlr.press/v9/agarwal10a/agarwal10a.pdf UR - https://proceedings.mlr.press/v9/agarwal10a.html AB - We study the problem of allocating stocks to dark pools. We propose and analyze an optimal approach for allocations, if continuous-valued allocations are allowed. We also propose a modification for the case when only integer-valued allocations are possible. We extend the previous work on this problem (Ganchev et al., 2009) to adversarial scenarios, while also improving over their results in the iid setup. The resulting algorithms are efficient, and perform well in simulations under stochastic and adversarial inputs. ER -
APA
Agarwal, A., Bartlett, P. & Dama, M.. (2010). Optimal Allocation Strategies for the Dark Pool Problem. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 9:9-16 Available from https://proceedings.mlr.press/v9/agarwal10a.html.

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