Lest We Forget: Learn from Out-of-Sample Forecast Errors When Optimizing Portfolios
研究发现投资组合优化中的历史预测误差并非完全随机，而是存在可预测模式，利用这些误差校准输入（类似经验贝叶斯学习）能提升样本外表现，使优化方法更可靠。
Abstract 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. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.