Why an Efficient Portfolio Optimizer Can Become an Error Maximizer
Mean-variance optimization appears precise, but its recommended portfolio can change radically after tiny changes to expected returns. Historical inputs once produced supposedly efficient portfolios dominated by Japanese and precious-metal stocks, exposing the danger of treating unstable estimates as reliable instructions.
- Optimization output is extremely sensitive to input assumptions.
- A one- or two-percent return adjustment can add or eliminate an asset class.
- Historical optimization can concentrate portfolios in assets near the end of a boom.
- False precision can magnify estimation errors.
“change the return of an asset class by percent or two in either direction, and it might completely dominate a portfolio or it might completely…”
“what some wags refer you know to to a mean variance optimizer is as an error maximizer.”