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Technical note: a robust perspective on transaction costs in portfolio optimization


Operations Research


Management Science and Operations

Authors / Editors

Olivares-Nadal A V;DeMiguel V


Publication Year



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-o 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.


Transaction costs; Estimation error; Robust optimization; Robust regression; Bayesian portfolios; Data-driven portfolios.

Available on ECCH


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