Expected returns and volatility tell you about individual assets. But portfolios are not built from individual assets in isolation — they're built from combinations of assets that interact with each other.

This is where correlation becomes the critical variable. Understanding how assets move together is arguably more important than knowing their individual expected returns.

What Is Correlation?

Correlation measures how two assets move in relation to each other. It ranges from -1 to +1:

• +1.0 (Perfect positive correlation): The two assets move in perfect lockstep. When one rises 5%, the other rises 5%. Holding both provides zero diversification benefit.
• 0.0 (No correlation): The assets have no systematic relationship. One might rise while the other falls, or vice versa. Combining them can significantly reduce portfolio volatility.
• -1.0 (Perfect negative correlation): The assets move in perfect opposition. When one rises, the other falls by the same amount. This is rare in practice but would provide maximum diversification.

The lower the correlation between assets, the more you benefit from holding them together. This is the mathematical foundation of diversification.

Why Correlation Matters More Than You Think

Consider two portfolios:

• Portfolio A: 100% in a single U.S. equity ETF
• Portfolio B: 50% in U.S. equities, 50% in gold

Both might have similar expected returns. But Portfolio B will almost certainly have lower volatility — because gold and stocks are uncorrelated. When stocks crash, gold often rises, cushioning the blow.

This is the free lunch of investing: you can reduce risk without necessarily sacrificing returns if you combine assets with low correlations.

The Challenge: Correlations Are Not Constant

Here's the complication: correlations shift with changing economic regimes and market structures.

A correlation observed in the last decade might not hold in the next if conditions change. For example, the stock-bond correlation was negative for most of the 2010s (stocks up = bonds down, and vice versa), but it turned positive in 2022 when both stocks and bonds fell together during the inflation shock.

Voya Investment Management notes that over 20 years, the average stock-bond correlation was approximately zero — but it oscillated from -0.10 in normal periods to +0.07 in turbulent periods. Relying blindly on long-run averages "offers little insight" if we don't account for these regime differences.

For the estimates below, I've combined historical analysis with forward-looking research from institutions like Morningstar, BlackRock, and State Street Global Advisors. Where ETFs are new (like MNZL or WSHR), I've used proxy indices — the underlying benchmarks they track — to estimate behavior. The goal is to capture long-run structural relationships, not short-term noise.