Censored Exploration and the Dark Pool Problem

Kuzman Ganchev; Michael Kearns; Yuriy Nevmyvaka; Jennifer Wortman Vaughan

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arXiv · 1205.2646

Censored Exploration and the Dark Pool Problem

Kuzman Ganchev·Michael Kearns·Yuriy Nevmyvaka·Jennifer Wortman Vaughan

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Abstract

We introduce and analyze a natural algorithm for multi-venue exploration from censored data, which is motivated by the Dark Pool Problem of modern quantitative finance. We prove that our algorithm converges in polynomial time to a near-optimal allocation policy; prior results for similar problems in stochastic inventory control guaranteed only asymptotic convergence and examined variants in which each venue could be treated independently. Our analysis bears a strong resemblance to that of efficient exploration/ exploitation schemes in the reinforcement learning literature. We describe an extensive experimental evaluation of our algorithm on the Dark Pool Problem using real trading data.

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Kuzman Ganchev, Michael Kearns, Yuriy Nevmyvaka, Jennifer Wortman Vaughan. 2012-05-09. Censored Exploration and the Dark Pool Problem. https://arxiv.org/abs/1205.2646

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