Using Dynamic Programming to Optimally Rebalance Portfolios

Abstract

The authors propose a different framework that quantifies the cost of a rebalancing strategy in terms of risk-adjusted returns net of transaction costs. They then derive an optimal rebalancing strategy that actively seeks to minimize that cost. Certainty equivalents and the transaction costs associated with a policy to define a cost-to-go function are used, with the expected cost-to-go minimized using dynamic programming. They apply Monte Carlo simulations to demonstrate that their method outperforms traditional rebalancing strategies like monthly, quarterly, annual, and 5% tolerance rebalancing. They also show the robustness of our method to model error by performing sensitivity analyses.