Mean Variance Optimization
Mean Variance Optimization — Classic Markowitz optimization — fragile to inputs.
Definition
Mean Variance Optimization refers to classic Markowitz optimization — fragile to inputs. Keep that definition fixed when comparing series, managers, or regimes — renaming the same tape does not create a new signal.
Why it matters
It shows up in factor research, attribution, and capacity debates — whether a return slice is skill, style, or fee drag. When classic Markowitz optimization — fragile to inputs shifts, related hedges, limits, and narratives usually need an explicit update rather than a quiet assumption.
Case
Suppose a desk is positioned for the opposite of what mean variance optimization is saying. If classic Markowitz optimization — fragile to inputs moves against that book, the first question is not “is the story clever?” but whether size, hedges, and stop logic still match the observation.
How to read it
Check definition stability across universes, costs, and regimes before treating a backtest as portable. Prefer a short written null hypothesis for Mean Variance Optimization: what would falsify the current reading in the next window?