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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.
Ganchev, Kuzman; Kearns, Michael J.; Nevmyvaka, Yuriy; and Wortman Vaughn, Jennifer, "Censored Exploration and the Dark Pool Problem" (2010). Departmental Papers (CIS). Paper 658.
Date Posted: 24 July 2012