Technical Note—A Robust Perspective on Transaction Costs in Portfolio Optimization

Abstract

We prove that the portfolio problem with transaction costs is equivalent to three different problems designed to alleviate the impact of estimation error: a robust portfolio optimization problem, a regularized regression problem, and a Bayesian portfolio problem. Motivated by these results, we propose a data-driven approach to portfolio optimization that tackles transaction costs and estimation error simultaneously by treating the transaction costs as a regularization term to be calibrated. Our empirical results demonstrate that the data-driven portfolios perform favorably because they strike an optimal trade-off between rebalancing the portfolio to capture the information in recent historical return data and avoiding the large transaction costs and impact of estimation error associated with excessive trading. The online companion is available at https://doi.org/10.1287/opre.2017.1699 .