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Dynamic Portfolio Choice with Linear Rebalancing Rules

  • Ciamac C. Moallemi and Mehmet Sağlam

Abstract

We consider a broad class of dynamic portfolio optimization problems that allow for complex models of return predictability, transaction costs, trading constraints, and risk considerations. Determining an optimal policy in this general setting is almost always intractable. We propose a class of linear rebalancing rules and describe an efficient computational procedure to optimize with this class. We illustrate this method in the context of portfolio execution and show that it achieves near optimal performance. We consider another numerical example involving dynamic trading with mean-variance preferences and demonstrate that our method can result in economically large benefits.

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Corresponding author

* Moallemi, ciamac@gsb.columbia.edu, Graduate School of Business, Columbia University; Sağlam (corresponding author), mehmet.saglam@uc.edu, Lindner College of Business, University of Cincinnati.

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We are grateful for helpful comments from an anonymous referee, David Brown, Stephen Brown (the editor), Sylvain Champonnois (discussant), Michael Sotiropoulos, and conference participants at the 2011 Annual Conference on Advances in the Analysis of Hedge Fund Strategies at Imperial College London. Moallemi acknowledges the support of National Science Foundation (NSF) grant CMMI-1235023. Sağlam acknowledges support from the Eugene M. Lang Doctoral Student Grant.

Footnotes

References

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Dynamic Portfolio Choice with Linear Rebalancing Rules

  • Ciamac C. Moallemi and Mehmet Sağlam

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