Dynamic Portfolio Choice with Linear Rebalancing Rules∗

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. ∗Sağlam acknowledges support from the Eugene M. Lang Doctoral Student Grant. Moallemi acknowledges the support of NSF grant CMMI-1235023. We are grateful for helpful comments from David Brown, Sylvain Champonnois (discussant), Michael Sotiropoulos, and conference participants at the 6th Annual Conference on Advances in the Analysis of Hedge Fund Strategies at Imperial College London. 1 This paper has been accepted for publication and will appear in a revised form, subsequent to peer review and/or editorial input by Cambridge University Press, in Journal of Financial and Quantitative Analysis published by Cambridge University Press. © Copyright 2015, Foster School of Business, University of Washington.