Illustrative example: a representative scenario, not a customer story
Financial Portfolio Optimization
Investment firm uses stochastic acceptability analysis to handle weight uncertainty in ranking 20 portfolio configurations across risk-return criteria.
The decision problem
Twenty portfolio configurations are ranked on return, risk and liquidity criteria. The honest position is that the weights are not known: one investor weights drawdown heavily, another weights liquidity, and there is no single correct vector. Fixing one weight vector produces a ranking that looks precise and is not.
What the decision matrix looks like
The typical shape of a study like this. Your own matrix can be larger or smaller; nothing here is fixed.
20
Alternatives
5
Criteria
Weight uncertainty, stochastic
Uncertainty type
| # | Criterion |
|---|---|
| C1 | Expected return |
| C2 | Volatility |
| C3 | Maximum drawdown |
| C4 | Liquidity |
| C5 | Fee ratio |
Which method, and why
SMAA samples the whole weight space instead of one vector and reports, for each alternative, how often it lands in each rank. What comes out is a rank acceptability rather than a single position, which is the answer that matches the question. MARCOS adds one deterministic ranking against ideal and anti-ideal reference points, so the stochastic result has a fixed baseline to sit against.
What you get out
Ranking and scores
Every alternative with its score, its rank and the intermediate matrices that produced them.
Weight sensitivity
One criterion weight moves at a time, so you see exactly where the leader changes.
Method agreement
When more than one ranking method is run, the orders are compared with Spearman rho and Kendall W.
Report and citation
PDF, DOCX and XLSX output carrying the seminal source of every method used.
Run this flow on your own data
Load the matrix, choose the weighting and ranking methods, read the sensitivity.