You will be able to find holdings that are really one bet and adjust sizing to allow for it.
When Marcus ran the oil shock through his holdings in lesson 4.8, Run a geopolitical scenario through your holdings, three lines moved together: his bank, his STI ETF and his S-REIT. He wrote that he'd set a combined limit for them in his risk rulebook. Lesson 11.3, Position and sector limits, and concentration risk, gave him limits for single stocks, sectors and countries, and the three holdings passed most of them individually. Together, they were one large bet he'd never sized.
This lesson finds those bets and caps them. Holdings and weights are Marcus's made-up figures from lesson 11.3. Correlations are made up, apart from the two from lesson 2.4.
A correlation cluster is a group of holdings that react to the same underlying force, so that in a shock they behave like one position. The force might be interest rates, oil, the chip cycle, regional growth or the US dollar. The holdings can sit in different sectors, trade on different exchanges and carry different names.
Lesson 2.4, Beta, correlation and why diversification shrinks in a crisis, measured two of Marcus's pairs. His bank and STI ETF had a correlation of about 0.88, and his chip designer and world ETF about 0.82. Pairs above 0.8, that lesson said, behave like one position. This lesson turns that observation into a sizing rule.
Start with your holdings and ask of each: what one thing, if it went badly, would hurt this most? Then group holdings with the same answer.
Marcus's answers gave him four clusters. His world ETF, at about 45%, is driven by global shares, which is the core of his allocation. His SGD bond fund and T-bills, about 19.8% together, are driven by Singapore interest rates. His STI ETF, bank and S-REIT are all driven by Singapore rates and regional growth: the bank through its lending margin and loan losses, the REIT through its borrowing costs and property values, and the STI ETF because banks are its largest weight. Together they're S$48,000, about 23.8% of the portfolio. And his chip designer and Larkspur are both driven by the chip cycle, S$23,000 or about 11.4%.
Notice where Larkspur went. It's listed on SGX and sits in a Singapore precision engineering category, so a label-based view would have put it with his other Singapore holdings. Its main driver is chipmakers' spending, though, and in a chip downturn it would fall with his US chip designer, not with Singapore banks. Lesson 4.3, Semiconductor supply chains and export controls, found the two moving together on the day new export rules were announced.
Correlations from your own data help confirm the grouping. With made-up figures, Larkspur's monthly returns had a correlation of about 0.7 with the chip designer's and about 0.4 with the STI ETF's. The S-REIT's correlation with the STI ETF was about 0.6 in calm years. Where your data and your judgement disagree, ask why before you trust either.
Once you know the clusters, size each one as if it were a single holding. Marcus set a cluster limit of 20% of the portfolio for any group of holdings that share a driver, apart from his core world equity and bond funds, whose sizes are set by his allocation.
His Singapore rates and regional growth cluster, at 23.8%, breaks it. The fix doesn't have to come only from selling the obvious name. Trimming his bank back to its 5% target, as lesson 11.3 already required, takes the cluster to about S$42,100, or 20.8%, still over. Trimming the S-REIT back to its 5% target as well brings it to about S$40,200, about 19.9%, just inside. Both trims also help the Singapore country limit from lesson 11.3. His chip cluster, at 11.4% before any trims, passes, but its look-through total through the world ETF sits on the sector limit, so the chip designer's trim to its 4% target helps there too.
Correlations are measured in ordinary months, and ordinary months flatter them. In a market-wide fall, investors sell whatever they can, and holdings that moved separately in calm years fall together, as lesson 2.4 showed for 2008 and March 2020.
So treat calm-period correlations as a minimum. The S-REIT's 0.6 with the STI ETF could become 0.9 in a crisis, which means the cluster's combined loss would be close to the sum of its parts rather than smaller. In the made-up 2020-style fall that lesson 11.6, Stress test against past crises and made-up shocks, runs, Marcus's STI ETF, bank and S-REIT together lose about S$15,500, about 7.7% of the whole portfolio from one cluster. That's the number the cluster limit is really about.
There's also a cluster you can't size away: the one driven by shares as a whole. In a severe crash, almost every equity holding joins it. That's not a failure of the cluster method. It's the equity risk Marcus chose when he set an 80% allocation, and it's handled by his allocation and rebalancing rules from Build and run an ETF portfolio, lesson 6.2, Calendar or threshold rebalancing.
He added one line to his rulebook: "Any group of holdings sharing one main driver is capped at 20% of the portfolio, counted as if it were a single position; core world equity and bonds are set by my allocation instead."
Open your list of holdings with their values and get ready to write beside each one the single force that would hurt it most.
Group your holdings into clusters by main driver and total each cluster's weight against your position limit.
Junxiong-WFG Organisation is an authorised representative of AIA Financial Advisers Private Limited (Reg. No. 201715016G).