6. concLusion

While there has been longstanding interest in quantitative finance in the use of models from machine learning and related fields, they are often applied towards the attempt to predict directional price movements, or in the parlance of the field, to “generate alpha” (outperform the market). Here we have instead focused on a problem in what is often called algorithmic trading—where one seeks to optimize properties of a specified trade, rather than decide what to trade in the first place—in the recently introduced dark pool mechanism. In part because of the constraints imposed by the mechanism and the structure of the problem, we have been able to adapt and blend methods from statistics and reinforcement learning in the development of a simple, efficient, and provably effective algorithm. We expect there will be many more applications of machine learning methods in algorithmic trading in the future.

acknowledgments

We are grateful to Curtis Pfeiffer and Andrew Westhead for valuable conversations and to Bobby Kleinberg for introducing us to the literature on the newsvendor problem.

1. akritas, m.g. nonparametric survival analysis. Stat. Sci. 19, 4 (2004), 615–623.

2. alon, n., spencer, J. The Probabilistic Method, 2nd Edition. Wiley, new york, 2000.

3. bogoslaw, d. big traders dive into dark pools. business Week article, available at: http://www.businessweek.com/ investor/content/oct2007/pi2007102_ 394204.htm, 2007.

4. brafman, r., tennenholtz, m. r-maX—a general polynomial time algorithm for near-optimal reinforcement learning. J. Mach. Learn. Res. 3 (2003), 213–231.

5. carrie, c. illuminating the new dark influence on trading and u.s. market structure. J. Trading 3, 1 (2008), 40–55.

6. cesa-bianchi, n., lugosi, g. Prediction, Learning, and Games. cambridge university Press, 2006.

7. domowitz, i., finkelshteyn, i., yegerman, h. cul de sacs and highways: an optical tour of dark pool trading performance. J. Trading 4, 1 (2009), 16–22.

References Kuzman Ganchev ([e-mail]. edu), university of Pennsylvania.

Yuriy nevmyvaka ( [e-mail]), university of Pennsylvania.

8. foldes, a., rejto, l. strong uniform consistency for nonparametric survival curve estimators from randomly censored data. Ann. Stat. 9, 1 (1981), 122–129.

9. ganchev, k., kearns, m. nevmyvaka, y., vaughan, J. W. censored exploration and the dark pool problem. in Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, 2009.

10. huh, W.t., levi, r., rusmevichientong, P., orlin, J. adaptive data-driven inventory control policies based on kaplan–meier estimator. Preprint available at http:// legacy.orie.cornell.edu/~paatrus/ psfiles/km-myopic.pdf, 2009.

11. kaplan, e.l., meier, P. nonparametric estimation from incomplete observations. J. Am. Stat. Assoc. 53 (1958), 457–481.

12. kearns, m., singh, s. near-optimal reinforcement learning in polynomial time. Mach. Learn. 49 (2002), 209–232.

13. Peterson, a.v. kaplan-meier estimator. in Encyclopedia of Statistical Sciences. Wiley, 1983.

Michael Kearns ([e-mail]. edu), university of Pennsylvania.

Jennifer Wortman Vaughan (jenn@seas. harvard.edu), harvard university.

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