Portfolio optimization with groupwise selection

Namhyoung Kim, Suvrit Sra

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Portfolio optimization in the presence of estimation error can be stabilized by incorporating norm-constraints; this result was shown by DeMiguel et al. (A generalized approach to portfolio optimization: improving performance by constraining portfolio norms, Management Science, 5, 798-812, 2009), who reported empirical performance better than numerous competing approaches. We extend the idea of norm-constraints by introducing a powerful enhancement, grouped selection for portfolio optimization. Here, instead of merely penalizing norms of the assets being selected, we penalize groups, where within a group assets are treated alike, but across groups, the penalization may differ. The idea of groupwise selection is grounded in statistics, but to our knowledge, it is novel in the context of portfolio optimization. Novelty aside, the real benefits of groupwise selection are substantiated by experiments; our results show that groupwise asset selection leads to strategies with lower variance, higher Sharpe ratios, and even higher expected returns than the ordinary norm-constrained formulations.

Original languageEnglish
Pages (from-to)442-448
Number of pages7
JournalIndustrial Engineering and Management Systems
Issue number4
StatePublished - 1 Dec 2014
Externally publishedYes


  • Asset class
  • Group-norm
  • Portfolio optimization


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