Online Portfolio Selection with Group Sparsity


  • Puja Das University of Minnesota, Twin Cities
  • Nicholas Johnson University of Minnesota, Twin Cities
  • Arindam Banerjee University of Minnesota, Twin Cities



Online learning, Portfolio selection, Group lasso, Non-smooth convex optimization, Alternating direction method of multipliers


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.




How to Cite

Das, P., Johnson, N., & Banerjee, A. (2014). Online Portfolio Selection with Group Sparsity. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1).



Main Track: Machine Learning Applications