| Sparse and stable Markowitz portfolios. | |
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MedLine Citation:
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PMID: 19617537 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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We consider the problem of portfolio selection within the classical Markowitz mean-variance framework, reformulated as a constrained least-squares regression problem. We propose to add to the objective function a penalty proportional to the sum of the absolute values of the portfolio weights. This penalty regularizes (stabilizes) the optimization problem, encourages sparse portfolios (i.e., portfolios with only few active positions), and allows accounting for transaction costs. Our approach recovers as special cases the no-short-positions portfolios, but does allow for short positions in limited number. We implement this methodology on two benchmark data sets constructed by Fama and French. Using only a modest amount of training data, we construct portfolios whose out-of-sample performance, as measured by Sharpe ratio, is consistently and significantly better than that of the naïve evenly weighted portfolio. |
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Authors:
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Joshua Brodie; Ingrid Daubechies; Christine De Mol; Domenico Giannone; Ignace Loris |
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Publication Detail:
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Type: Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S. Date: 2009-07-15 |
Journal Detail:
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Title: Proceedings of the National Academy of Sciences of the United States of America Volume: 106 ISSN: 1091-6490 ISO Abbreviation: Proc. Natl. Acad. Sci. U.S.A. Publication Date: 2009 Jul |
Date Detail:
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Created Date: 2009-08-11 Completed Date: 2009-09-28 Revised Date: 2010-01-29 |
Medline Journal Info:
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Nlm Unique ID: 7505876 Medline TA: Proc Natl Acad Sci U S A Country: United States |
Other Details:
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Languages: eng Pagination: 12267-72 Citation Subset: IM |
Affiliation:
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Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ 08544-1000, USA. |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms* Computer Simulation* Industry / standards Models, Theoretical Reproducibility of Results |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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