Reverse optimisation
Working backwards from market-capitalisation weights, a covariance matrix and a level of risk aversion to the expected returns that would make those weights optimal. A neutral starting point that views can then adjust.
Where you will meet it
Reverse optimisation appears in 3 learning outcomes on the CFA syllabus. A few of them:
- Explain how the global market portfolio serves as a neutral starting point for asset allocation
“Reverse optimisation of market weights yields implied equilibrium returns that can feed an optimiser.”
- Explain how mean–variance optimisation is used to set an asset allocation and assess its strengths and weaknesses
“Remedies include reverse optimisation, Black–Litterman views, resampling, and sensible constraints.”
- Propose and defend an asset allocation that uses the global market portfolio as its reference point
“Reverse optimisation gives implied equilibrium returns consistent with those weights, Π = λ × Σ × wmkt; Black–Litterman then blends in the investor's views with stated confidence, producing tilts rather than extreme bets.”
See also
Practise it
Practise reverse optimisation until it is automatic: questions that explain every wrong option.
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