A data-driven look at how the 11 GICS sectors and major asset classes move together over the trailing 3-year period (2023-03-11 to 2026-03-11). Discover which sectors are uncorrelated when it matters most.
A stock correlation matrix measures how closely the daily returns of two assets move in tandem. A correlation of +1.0 means they move perfectly together, 0.0 means they are completely independent (uncorrelated), and -1.0 means they move in exact opposite directions.
Post-2020 market dynamics fundamentally changed the relationships between sectors. Tech dominance, inflation shocks, and shifting interest rate regimes mean that old assumptions about diversification no longer hold true. The interactive heatmap above uses daily return data from 2023-03-11 to 2026-03-11 across 750 trading days to show you the reality of today's market.
Looking at the data, the true diversifiers are assets with correlations near zero relative to the broader market. Real estate, utilities, and consumer staples often show lower correlations to high-beta sectors like technology and consumer discretionary. Furthermore, alternative asset classes like gold and commodities provide some of the lowest correlations to the S&P 500, offering genuine portfolio ballast.
A stock correlation matrix is a table showing correlation coefficients between different variables—in this case, stock market sectors or asset classes. Each cell in the matrix represents the correlation between two specific assets, helping investors see which investments move together and which are independent.
Find the row of one asset and follow it to the column of another. The intersecting cell shows a number between -1.0 and 1.0. Positive numbers (usually shaded green) mean they move together. Values near zero (often dark) mean they are uncorrelated. Negative numbers (shaded red) mean they move in opposite directions.
Based on recent data, defensive sectors like Utilities and Consumer Staples tend to have the lowest correlation to the broader S&P 500. Additionally, specific asset classes outside of equities—such as Gold, Commodities, and Total Bond Market indices—often exhibit correlations near zero or slightly negative relative to the S&P 500.
Post-2020, macro factors like shifting interest rates and concentrated mega-cap tech performance have caused many sectors to move in lockstep, increasing overall market correlation. However, assets highly sensitive to physical supply chains or specific interest rate curves (like Energy or Real Estate) have occasionally decoupled from the broader market.
True diversification isn't just owning many different stocks; it's owning assets that don't all drop at the same time. If you own 10 different technology and consumer discretionary ETFs, but they all have a 0.9 correlation to each other, a market shock will still pull your entire portfolio down. Uncorrelated assets smooth out long-term returns.
This study calculates Pearson correlation coefficients using daily adjusted closing prices over the trailing 3-year period (2023-03-11 to 2026-03-11). The data includes 750 trading days. We use the 11 SPDR GICS Sector ETFs as proxies for sector performance, SPY for the S&P 500, BND for the Total Bond Market, GLD for Gold, VNQ for broad Real Estate, and PDBC for broad Commodities. Missing data points are removed from the vector pairs to ensure exact matching periods.
Westmount Fundamentals. "Stock Correlation Matrix 2026: What Actually Diversifies?." westmountfundamentals.com/stock-correlation-matrix-2026, 2026.