We develop a novel Mean-Max Drawdown portfolio optimization approach using buy-and-hold portfolios. The optimization is performed utilizing a multi-objective evolutionary algorithm on a sample of S&P 100 constituents. Our optimization procedure provides portfolios with better Mean-Max Drawdown trade-offs compared to relevant benchmarks, regardless of the selected subsamples and market conditions. The superior performance of our approach is particularly pronounced in periods with reversing market trends (i.e. a market rally and a fall in the same subsample).

中文翻译：

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使用多目标进化算法对买入并持有投资组合进行均值-最大回撤优化

我们使用买入并持有投资组合开发了一种新颖的 Mean-Max Drawdown 投资组合优化方法。优化是利用多目标进化算法对标准普尔 100 指数成分样本进行的。与相关基准相比，我们的优化程序为投资组合提供了更好的 Mean-Max Drawdown 权衡，无论选择的子样本和市场条件如何。我们方法的卓越表现在市场趋势逆转时期（即同一子样本中的市场反弹和下跌）尤为明显。