Financials vs Regional Banks: Correlation Analysis
Pearson correlation of daily returns for Financials (XLF) and Regional Banks (KRE). Rolling windows, yearly breakdown, regression beta, and divergence analysis. Data window spans to (1,262 aligned observations).
What the Number Means
At 0.75, Financials (XLF) and Regional Banks (KRE) have a strong tendency to move together. Most daily moves align, though divergences are common enough that the relationship should not be treated as deterministic. A shared regime or macro factor is likely driving both.
Recent vs Long-Run Behavior
Recent correlation tracks the long-run relationship closely. No meaningful divergence. The historical pattern between Financials (XLF) and Regional Banks (KRE) is intact and should continue to serve as a reasonable baseline for positioning.
Statistical Details (1-Year Window)
| Pearson Correlation (r) | +0.748 |
| R-Squared (r²) | 0.559 |
| Beta (Financials (XLF) vs Regional Banks (KRE)) | 0.463 |
| Daily Volatility σ(Financials (XLF)) | 0.90% |
| Daily Volatility σ(Regional Banks (KRE)) | 1.46% |
| 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 Financials (XLF) returns on Regional Banks (KRE) returns. A beta above 1 means the first asset amplifies moves of the second.
Year-by-Year Correlation
| Year | Correlation | Strength | Observations |
|---|---|---|---|
| 2026 | +0.754 | Strong positive | 91 |
| 2025 | +0.809 | Very strong positive | 250 |
| 2024 | +0.783 | Strong positive | 252 |
| 2023 | +0.806 | Very strong positive | 250 |
| 2022 | +0.897 | Very strong positive | 251 |
| 2021 | +0.874 | Very 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 Financials (XLF) and Regional Banks (KRE), 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.