Censored exploration and the dark pool problem

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.

• Type: Journal
• Year: 2010
• Where: CACM
• Authors: Kuzman Ganchev, Yuriy Nevmyvaka, Michael Kearns, Jennifer Wortman Vaughan
• Keywords: Algorithm, CACM 2010, Dark Pool Problem, Modern Quantitative Finance