Empirical Portfolio Selection

author: László Györfi, Department of Computer Science and Information Theory, Budapest University of Technology and Economics
published: Aug. 21, 2009,   recorded: July 2009,   views: 7286


Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.


Dark pools are a relatively recent type of equities exchange in which transparency is deliberately limited in order to minimize the market impact of large-volume trades. The success and proliferation of dark pools has also led to a challenging and interesting problem in algorithmic trading --- namely, optimizing the distribution of a large trade over multiple competing dark pools. In this work we formalize this as a problem of multi-venue exploration from censored data, and provide a provably efficient and near-optimal algorithm for its solution. This algorithm and its analysis has much in common with well-studied algorithms for exploration-exploitation in reinforcement learning, and is evaluated on dark pool execution data from a large brokerage.

See Also:

Download slides icon Download slides: amlcf09_gyorfi_eps_01.pdf (1.1 MB)

Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: