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

• 成果类型: Article
• 署名作者: Olivares-Nadal, Alba V.; DeMiguel, Victor
• 署名单位: Universidad Pablo de Olavide; University of London; London Business School
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2017.1699
• 发表日期: 2018
• 页码: 733-739
• 关键词: selection; uncertainty; performance

摘要

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.