Pearson correlation of daily returns across major asset classes.
Green = assets move together, red = they move inversely.
Computing correlations...
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For informational purposes only. The data and visualizations on this page do not constitute financial advice, investment recommendations, or an offer to buy or sell any securities. Always do your own research and consult a qualified financial advisor before making investment decisions.
A correlation matrix displays the Pearson correlation coefficients between every pair of assets in a selected group. The coefficient ranges from -1 to +1: a value of +1 means two assets move perfectly in sync, -1 means they move in opposite directions, and 0 means their movements are unrelated. Investors use correlation matrices to build diversified portfolios, identify hedging opportunities, and understand how different asset classes behave relative to each other under various market conditions.
These assets tend to move together. A portfolio heavy in positively correlated assets will see amplified gains in bull markets but also amplified losses in downturns. Examples include U.S. large-cap stocks and U.S. mid-cap stocks.
These assets have little to no relationship. Combining uncorrelated assets is the foundation of diversification—when one zigs, the other doesn't necessarily zag, it just does its own thing. Gold and U.S. equities often show low correlation.
These assets tend to move in opposite directions. Negatively correlated assets can act as hedges in certain conditions—when one falls, the other rises, smoothing portfolio returns. Treasury bonds and stocks have historically shown negative correlation during stress periods.
True diversification means owning assets that don't all move together. If every asset in your portfolio has a correlation of +0.9 with every other asset, you effectively have a concentrated bet regardless of how many names you hold. Many portfolio managers aim to combine assets with low or negative correlations so that when one part of the portfolio declines, another part holds steady or rises. Use different timeframes (1M, 3M, 6M, 1Y) to check whether correlations are stable or shifting, as correlations tend to increase during market crises.
Pearson correlation is the most common measure of linear relationship between two variables. It is calculated using daily returns over the selected timeframe. A key limitation is that it only captures linear relationships—two assets could have a complex, non-linear relationship that Pearson misses. It also assumes returns are normally distributed, which isn't always true.
Yes. Correlations are not static. They shift as economic conditions, monetary policy, and market regimes change. Most notably, correlations tend to spike toward +1 during market panics (“correlation goes to 1 in a crisis”), reducing the benefit of diversification precisely when you need it most. Checking correlations across multiple timeframes helps identify these regime shifts.
Assets with correlations below 0.3 provide meaningful diversification benefits. Below 0.0 (negative correlation) is even better for hedging. The classic 60/40 stock-bond portfolio works because stocks and long-term bonds have historically shown low to negative correlation, though this relationship has varied in different interest rate environments.
Short timeframes (1M) capture recent market dynamics and are useful for tactical trading. Longer timeframes (6M, 1Y) smooth out noise and reveal structural relationships between asset classes. If a correlation is consistent across all timeframes, it's a stable relationship. If it varies widely, the relationship is regime-dependent.
