Portfolio optimization often struggles in realistic out-of-sample contexts. We deconstruct this stylized fact by comparing historical forecasts of portfolio optimization inputs with subsequent out-of-sample values. We confirm that historical forecasts are imprecise guides of subsequent values, but we discover the resultant forecast errors are not entirely random. They have predictable patterns and can be partially reduced using their own history. Learning from past forecast errors to calibrate inputs (akin to empirical Bayesian learning) generates portfolio performance that reinforces the case for optimization. Furthermore, the portfolios achieve performance that meets expectations, a desirable yet elusive feature of optimization methods.

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不要忘记：在优化投资组合时从样本外预测错误中学习

投资组合优化通常在现实的样本外环境中挣扎。我们通过将投资组合优化输入的历史预测与随后的样本外值进行比较来解构这个典型的事实。我们确认历史预测是后续值的不精确指南，但我们发现由此产生的预测误差并非完全随机。它们具有可预测的模式，并且可以使用它们自己的历史来部分减少。从过去的预测误差中学习以校准输入（类似于经验贝叶斯学习）产生的投资组合性能加强了优化的案例。此外，投资组合实现了符合预期的性能，这是优化方法的理想但难以捉摸的特征。