Parameter-free robust optimization for the maximum-Sharpe portfolio problem

Deepayan Chakrabarti

European Journal of Operational Research, 2021, vol. 293, issue 1, 388-399

Abstract: How can we optimize for the Sharpe ratio if we only have limited training data? Estimates of mean asset returns are noisy, and this noise hurts the out-of-sample Sharpe ratio of current methods. The minimum-variance portfolio, which ignores mean returns, often has a better Sharpe ratio. We develop a parameter-free and scalable method called AlphaRob for this problem. AlphaRob ’s portfolio is a convex combination of two prespecified portfolios. To select the best combination, AlphaRob fuses robust optimization with a new notion of a portfolio’s regret that accounts for the training data’s size. Our analysis only needs mild assumptions on the distribution of asset returns. AlphaRob significantly outperforms competing methods on several simulated and real-world datasets, even after adjusting for transaction costs. AlphaRob is 7.5% better on average than the nearest competitor, and 28% better than the next-best combination portfolio method. Using our regret of regret, we are also able to explain the performance of the minimum-variance portfolio.

Keywords: Finance; Robust optimization; Sharpe ratio; Portfolio optimization (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:293:y:2021:i:1:p:388-399

DOI: 10.1016/j.ejor.2020.11.052

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