Optimal Rebalancing Strategy Using Dynamic Programming for Institutional Portfolios

Institutional fund managers generally rebalance using ad hoc methods such as calendar basis or tolerance band triggers. We propose a different framework that quantifies the cost of a rebalancing strategy in terms of risk-adjusted returns net of transaction costs. We then develop an optimal rebalancing strategy that actively seeks to minimize that cost. We use certainty equivalents and the transaction costs associated with a policy to define a cost-to-go function, and we minimize this expected cost-to-go using dynamic programming. We apply Monte Carlo simulations to demonstrate that our method outperforms traditional rebalancing strategies like monthly, quarterly, annual, and 5% tolerance rebalancing. We also show the robustness of our method to model error by performing sensitivity analyses. Laboratory for Information and Decision Systems, Massachusetts Institute of Technology Research Laboratory of Electronics, Massachusetts Institute of Technology Corresponding author: [email protected]