Energy (XLE) vs Financials (XLF): Correlation Analysis
Pearson correlation of daily returns for Energy (XLE) and Financials (XLF). Rolling windows, yearly breakdown, regression beta, and divergence analysis. Data window spans to (1,262 aligned observations).
What the Number Means
With a correlation of -0.19, Energy (XLE) and Financials (XLF) are essentially uncorrelated at daily frequency. Either the relationship operates at a different time horizon or the shared driver has been dominated by idiosyncratic noise during the observation window.
Recent vs Long-Run Behavior
The historical positive relationship between Energy (XLE) and Financials (XLF) has inverted. Recent 90-day correlation is -0.19 versus a long-run reading of 0.49. This kind of decoupling tends to mark regime transitions. Often one asset is responding to a newly dominant driver while the other is anchored to the old narrative.
Statistical Details (1-Year Window)
| Pearson Correlation (r) | +0.036 |
| R-Squared (r²) | 0.001 |
| Beta (Energy (XLE) vs Financials (XLF)) | 0.049 |
| Daily Volatility σ(Energy (XLE)) | 1.22% |
| Daily Volatility σ(Financials (XLF)) | 0.90% |
| Observations | 252 |
Correlation measures directional co-movement; R² quantifies the fraction of variance explained by the linear relationship. Beta is the slope coefficient from regressing Energy (XLE) returns on Financials (XLF) returns. A beta above 1 means the first asset amplifies moves of the second.
Year-by-Year Correlation
| Year | Correlation | Strength | Observations |
|---|---|---|---|
| 2026 | -0.179 | Essentially uncorrelated | 91 |
| 2025 | +0.600 | Strong positive | 250 |
| 2024 | +0.487 | Moderate positive | 252 |
| 2023 | +0.534 | Moderate positive | 250 |
| 2022 | +0.432 | Moderate positive | 251 |
| 2021 | +0.759 | Strong positive | 168 |
Year-by-year correlation reveals how the relationship has held up across different macro regimes. Sharp year-over-year swings in correlation often mark the transition between stress and calm periods.
Rolling 90-Day Extremes
Extremes in rolling 90-day correlation often coincide with regime changes, forced deleveraging, or the arrival of a dominant new macro theme that overwhelms normal relationships.
Methodology
Correlations are computed on daily log-adjacent returns for Energy (XLE) and Financials (XLF), aligned on shared trading dates. We use the Pearson product-moment coefficient, which measures the linear relationship between two return series.
Windows are the most recent N observations for 30D, 90D, and 1Y (252 trading days); the 5Y figure uses all aligned data up to 1,260 observations. Beta is the OLS slope from regressing the first series on the second. Data updates daily with a 24-hour revalidation cadence.
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Get daily macro analysis on shifting correlations, regime transitions, and cross-asset signals.
Correlation is not causation and backward-looking statistics can fail when regimes shift. Positions sized on historical correlation assumptions should be stress-tested against scenarios where the relationship breaks. For informational purposes only.