Durable Goods Orders vs S&P 500: Correlation Analysis
Pearson correlation of daily returns for Durable Goods Orders and S&P 500 ETF (SPY). Rolling windows, yearly breakdown, regression beta, and divergence analysis. Data window spans to (36 aligned observations).
What the Number Means
A correlation of -0.28 signals only a weak tendency to move in opposite directions. On most days the two move independently. Do not expect one to reliably predict the other. Look for conditional relationships within specific regimes or event windows.
Recent vs Long-Run Behavior
Recent correlation tracks the long-run relationship closely. No meaningful divergence. The historical pattern between Durable Goods Orders 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) | -0.280 |
| R-Squared (r²) | 0.078 |
| Beta (Durable Goods Orders vs S&P 500 ETF (SPY)) | -0.234 |
| Daily Volatility σ(Durable Goods Orders) | 4.27% |
| Daily Volatility σ(S&P 500 ETF (SPY)) | 5.11% |
| Observations | 36 |
Correlation measures directional co-movement; R² quantifies the fraction of variance explained by the linear relationship. Beta is the slope coefficient from regressing Durable Goods Orders 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 |
|---|---|---|---|
| 2025 | -0.794 | Strong negative | 6 |
| 2024 | +0.012 | Essentially uncorrelated | 8 |
| 2023 | -0.311 | Weak negative | 8 |
| 2022 | +0.183 | Essentially uncorrelated | 9 |
| 2021 | -0.523 | Moderate negative | 5 |
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.
Methodology
Correlations are computed on daily log-adjacent returns for Durable Goods Orders 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.