Abstract
In portfolio selection, it often might be preferable to focus on a few top performing industries/sectors to beat the market. These top performing sectors however might change over time. In this paper, we propose an online portfolio selection algorithm that can take advantage of sector information through the use of a group sparsity inducing regularizer while making lazy updates to the portfolio. The lazy updates prevent changing ones portfolio too often which otherwise might incur huge transaction costs. The proposed formulation leads to a non-smooth constrained optimization problem at every step, with the constraint that the solution has to lie in a probability simplex. We propose an efficient primal-dual based alternating direction method of multipliers algorithm and demonstrate its effectiveness for the problem of online portfolio selection with sector information. We show that our algorithm OLU-GS has sub-linear regret w.r.t. the best fixed and best shifting solution in hindsight. We successfully establish the robustness and scalability of OLU-GS by performing extensive experiments on two real-world datasets.
Original language | English (US) |
---|---|
Title of host publication | Proceedings of the National Conference on Artificial Intelligence |
Publisher | AI Access Foundation |
Pages | 1185-1191 |
Number of pages | 7 |
ISBN (Electronic) | 9781577356783 |
State | Published - 2014 |
Event | 28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014 - Quebec City, Canada Duration: Jul 27 2014 → Jul 31 2014 |
Publication series
Name | Proceedings of the National Conference on Artificial Intelligence |
---|---|
Volume | 2 |
Other
Other | 28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014 |
---|---|
Country/Territory | Canada |
City | Quebec City |
Period | 7/27/14 → 7/31/14 |
Bibliographical note
Publisher Copyright:Copyright © 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.