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.
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
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yegerman, h. cul de sacs and
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Kuzman Ganchev (firstname.lastname@example.org.
edu), university of Pennsylvania.
Yuriy nevmyvaka ( email@example.com),
university of Pennsylvania.
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Michael Kearns (firstname.lastname@example.org.
edu), university of Pennsylvania.
Jennifer Wortman Vaughan (jenn@seas.
harvard.edu), harvard university.
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