Nested clustered optimization is an allocation method that splits one hard optimisation into several small ones: it groups the instruments that move alike, finds the lowest-risk mix inside each group, treats each group as a single instrument, and then finds the lowest-risk mix across the groups.
Also seen as: NCO
How does Fincanva handle it?
The method picker labels it "NCO · Nested clustered optimization" and describes it as "Optimizes inside each group of similar instruments, then across the groups".
- Nested clustered optimization is offered inside a single strategy only, across its instruments; a Combined does not offer it.
- It has no settings of its own. It reads the calculation window (In-sample, 12 months by default) and the Covariance matrix choice — see covariance matrix.
- Weights are never negative.
- With most covariance estimators — including the recommended Ledoit-Wolf · constant correlation — every instrument must have moved in price at some point inside the window: an instrument whose price stayed flat for the whole window, such as a suspended listing, stops the backtest rather than receiving a weight. Two estimators are the exception; covariance matrix names them.
- Weights are recomputed at every rebalance from the window ending on that date.
Why split the optimisation into groups?
Because estimation errors spread through a single large optimisation. When several instruments are almost identical, a minimum-risk optimiser over all of them at once can turn tiny differences in their measured correlations into large offsetting bets. Inside a small group the problem is well behaved, and the groups are then compared as a handful of synthetic instruments — so a measurement error inside one group cannot move the weights inside another.
- the instrument's final share of the capital
- instrument i's weight inside its group k
- group k's weight across the groups
An instrument's final weight is its weight inside its group multiplied by its group's weight. Both levels aim at the lowest-risk mix, the "Min volatility" end of the efficient frontier.
The number of groups is chosen from the data at each rebalance, not set by you.
When does Nested clustered optimization fit, and when not?
It fits a universe of many similar instruments — a basket of stocks that fall into sectors, say — where the groups are clear and a single optimisation would be most fragile. It fits less well in two cases:
- A universe with no clear groups. The two-stage answer then drifts further from what a single lowest-risk optimisation would give.
- A book built on a hedge. Two instruments that move in opposite directions are treated as maximally different and land in different groups, so the method cannot fully use the hedge between them.
And like any lowest-risk optimisation, the across-group step can give a whole high-risk group a weight close to zero.
Which plan includes Nested clustered optimization?
Included from Advanced upwards. See what each plan includes.
What does it look like in practice?
A strategy holds six instruments that fall into two groups of three: three bank stocks and three utility stocks. Inside the bank group, the lowest-risk mix is 50% / 30% / 20%; inside the utility group it is 40% / 40% / 20%. Treated as two synthetic instruments, the groups are then mixed for the lowest risk, which here comes out at 30% banks and 70% utilities.
The first bank stock's final weight is 50% × 30% = 15%; the first utility stock's is 40% × 70% = 28%. Every one of the six weights is built the same way, and together they add up to 100%.