Financials (XLF) vs S&P 500: Correlation Analysis
Pearson correlation of daily returns for Financials (XLF) and S&P 500 ETF (SPY). Rolling windows, yearly breakdown, regression beta, and divergence analysis. Data window spans to (1,262 aligned observations).
What the Number Means
At 0.69, Financials (XLF) and S&P 500 ETF (SPY) 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 S&P 500 ETF (SPY) is intact and should continue to serve as a reasonable baseline for positioning.
Statistical Details (1-Year Window)
| Pearson Correlation (r) | +0.690 |
| R-Squared (r²) | 0.477 |
| Beta (Financials (XLF) vs S&P 500 ETF (SPY)) | 0.815 |
| Daily Volatility σ(Financials (XLF)) | 0.90% |
| Daily Volatility σ(S&P 500 ETF (SPY)) | 0.77% |
| 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 S&P 500 ETF (SPY) returns. A beta above 1 means the first asset amplifies moves of the second.
Year-by-Year Correlation
| Year | Correlation | Strength | Observations |
|---|---|---|---|
| 2026 | +0.686 | Strong positive | 91 |
| 2025 | +0.840 | Very strong positive | 250 |
| 2024 | +0.627 | Strong positive | 252 |
| 2023 | +0.802 | Very strong positive | 250 |
| 2022 | +0.891 | Very strong positive | 251 |
| 2021 | +0.729 | 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 S&P 500 ETF (SPY), 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.