Footnote:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments September 25, 2020 erstellt
Description:
Portfolio optimization often struggles in realistic out-of-sample contexts. We de-constructthis stylized fact, comparing historical forecasts of portfolio optimization inputs withsubsequent out of sample values. We confirm that historical forecasts are imprecise guidesof subsequent values but also find the resulting 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)results in portfolio performance that reinforces the case for optimization. Furthermore,the portfolios achieve performance that meets expectations, a desirable yet elusive featureof optimization methods