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Parameter-free robust optimization for the maximum-Sharpe portfolio problem
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DOI：10.1016/j.ejor.2020.11.052.png)
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摘要
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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. (C) 2020 Elsevier B.V. All rights reserved.
Keyword:
Finance
Robust optimization
Sharpe ratio
Portfolio optimization
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6
论文数:
2.2W
被引数:
6.4W
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