Retail Sales vs Consumer Sentiment: Correlation Analysis
Pearson correlation of daily returns for Retail Sales (ex Food Svc) and Consumer Sentiment (Michigan). Rolling windows, yearly breakdown, regression beta, and divergence analysis. Data window spans to (57 aligned observations).
What the Number Means
With a correlation of -0.01, Retail Sales (ex Food Svc) and Consumer Sentiment (Michigan) 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
Recent correlation tracks the long-run relationship closely. No meaningful divergence. The historical pattern between Retail Sales (ex Food Svc) and Consumer Sentiment (Michigan) is intact and should continue to serve as a reasonable baseline for positioning.
Statistical Details (1-Year Window)
| Pearson Correlation (r) | -0.006 |
| R-Squared (r²) | 0.000 |
| Beta (Retail Sales (ex Food Svc) vs Consumer Sentiment (Michigan)) | -0.001 |
| Daily Volatility σ(Retail Sales (ex Food Svc)) | 1.02% |
| Daily Volatility σ(Consumer Sentiment (Michigan)) | 7.05% |
| Observations | 57 |
Correlation measures directional co-movement; R² quantifies the fraction of variance explained by the linear relationship. Beta is the slope coefficient from regressing Retail Sales (ex Food Svc) returns on Consumer Sentiment (Michigan) returns. A beta above 1 means the first asset amplifies moves of the second.
Year-by-Year Correlation
| Year | Correlation | Strength | Observations |
|---|---|---|---|
| 2026 | — | Insufficient data | 3 |
| 2025 | +0.074 | Essentially uncorrelated | 12 |
| 2024 | -0.547 | Moderate negative | 12 |
| 2023 | +0.352 | Weak positive | 12 |
| 2022 | -0.169 | Essentially uncorrelated | 12 |
| 2021 | -0.092 | Essentially uncorrelated | 6 |
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 Retail Sales (ex Food Svc) and Consumer Sentiment (Michigan), 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.
Related Correlations
More Comparisons
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.