Based on current macro regime conditions and credit card loans (banks)'s historical behaviour in similar regimes, the model projects 1,142.06 by 2026-12-31 ( +5.1% from 1,086.32 today). The 68% confidence range is 956.01 to 1,328.11; the wider 95% range is 777.4 to 1,506.72. Methodology below the headline.
Credit Card Loans (Banks) Forecast 2026
Quantitative analysis from 1,303 observations of Credit Card Loans (Banks) history, joined to four universal macro regime classifications. Numbers are computed, not narrated.
Regime Scan[01/04]
Δ = divergence from +6.3% unconditional all-history average
Performance by Window[02]
| WINDOW | N | ANN RET | ANN VOL | RET/VOL | HIT % | TOTAL |
|---|---|---|---|---|---|---|
| 1Y | 53 | 4.25% | 2.05% | 2.07 | 69.2% | 4.24% |
| 3Y | 157 | 3.45% | 2.92% | 1.18 | 69.2% | 10.66% |
| 5Y | 261 | 7.85% | 2.59% | 3.03 | 76.5% | 45.71% |
| 10Y | 522 | 4.85% | 2.74% | 1.77 | 68.7% | 60.47% |
| All | 1,303 | 6.31% | 21.17% | 0.30 | 57.8% | 359.84% |
Annualized total return = (1 + total)^(1/years) - 1. Ret/Vol is the annualized return divided by annualized volatility (Sharpe-equivalent without risk-free subtraction). Hit % = pct of single periods that were positive.
Where We Are Now[03]
Forward Returns by Macro Regime[04]
How Credit Card Loans (Banks) has performed historically conditional on the prevailing macro regime. The current bucket is highlighted; +1Y averages drive the headline signal above.
| REGIME BUCKET | N | +30D | +90D | +1Y AVG | +1Y MED | HIT % |
|---|---|---|---|---|---|---|
| Low (<15) | 431 | 0.45% | 1.31% | 3.22% | 4.33% | 76.8% |
| Normal (15-25) | 627 | 1.52% | 4.90% | 9.46% | 5.59% | 83.8% |
| Elevated (25-40) | 194 | 0.35% | 0.83% | 13.77% | 7.40% | 78.1% |
| Extreme (>40) | 37 | -1.43% | -4.25% | -8.74% | -11.16% | 13.5% |
| REGIME BUCKET | N | +30D | +90D | +1Y AVG | +1Y MED | HIT % |
|---|---|---|---|---|---|---|
| Inverted (<0bps) | 160 | 0.78% | 2.46% | 5.68% | 6.67% | 81.9% |
| Flat (0-100bps) | 435 | 0.41% | 1.12% | 3.17% | 4.18% | 73.1% |
| Steep (>100bps) | 690 | 1.24% | 3.92% | 10.12% | 4.89% | 80.6% |
| REGIME BUCKET | N | +30D | +90D | +1Y AVG | +1Y MED | HIT % |
|---|---|---|---|---|---|---|
| Tight (<350bps) | 195 | 0.63% | 1.91% | 6.36% | 4.12% | 67.8% |
| Normal (350-500bps) | 279 | 0.75% | 2.17% | 4.98% | 6.72% | 83.9% |
| Stressed (>500bps) | 114 | 0.10% | 1.18% | 4.70% | 6.28% | 86.0% |
| REGIME BUCKET | N | +30D | +90D | +1Y AVG | +1Y MED | HIT % |
|---|---|---|---|---|---|---|
| Weak (bottom tercile) | 209 | 0.69% | 6.85% | 14.06% | 7.26% | 83.7% |
| Neutral (middle) | 256 | 2.41% | 3.94% | 12.36% | 3.65% | 77.4% |
| Strong (top tercile) | 529 | 0.37% | 1.06% | 3.79% | 4.62% | 74.5% |
Forward returns are forward-looking from each historical observation in the bucket; +252d corresponds to one trading year. Buckets with fewer than 5 forward-return observations are reported as n/a. These are conditional historical averages, not forecasts.
Lead-Lag Relationships[05]
For each universally-recognised leading indicator, the lag at which the daily-return correlation peaks. Positive lag means the anchor leads Credit Card Loans (Banks); negative means it lags.
| ANCHOR | ROLE | PEAK LAG | PEAK CORR | ZERO-LAG | RELATIONSHIP |
|---|---|---|---|---|---|
| Initial Jobless Claims | Labor leader | +3d | -0.290 | -0.163 | coincident |
| HY OAS Spread | Credit risk leader | +5d | -0.170 | 0.056 | leads target by 5d |
| Copper | Global growth proxy | +34d | 0.105 | 0.038 | weak |
| Trade-Weighted Dollar | FX driver | -7d | 0.093 | -0.021 | weak |
| 10Y Treasury Yield | Discount-rate driver | +44d | 0.090 | 0.008 | weak |
| Baa-10Y Spread | Credit risk (slow) | -8d | 0.089 | 0.010 | weak |
| VIX | Volatility leader | -7d | 0.085 | -0.002 | weak |
| NFCI | Financial conditions | +18d | -0.066 | 0.009 | weak |
| 10Y-2Y Yield Spread | Recession leader | -8d | -0.025 | -0.001 | weak |
| U-Mich Consumer Sentiment | Survey leader | 0d | 0.000 | 0.000 | weak |
Pearson correlation of daily returns over up to 25 years of overlapping history, searched across a ±60-day lag grid. Indicators classified as “weak” don't have meaningful predictive power at daily resolution; many of these (yield curve, NFCI, sentiment) lead at monthly/quarterly horizons instead.
Historical Analogs[06]
Periods where Credit Card Loans (Banks) sat at a similar percentile rank to today, with what happened over the next 30 / 90 / 252 trading days. Analogs are clustered to avoid double-counting nearby dates.
| DATE | VALUE | +30D | +90D | +1Y |
|---|---|---|---|---|
| Apr 30, 2025 | 1040.2647 | 0.44% | 0.94% | 4.80% |
| Jan 29, 2025 | 1046.0037 | 0.08% | -0.12% | 2.08% |
| Oct 30, 2024 | 1080.0072 | -0.19% | -3.07% | -2.21% |
| Jul 31, 2024 | 1067.4365 | 0.40% | 0.98% | -1.67% |
| May 1, 2024 | 1056.3377 | 0.65% | 1.45% | -1.52% |
Worst Historical Drawdown[07]
Cross-Asset Correlations · 1Y[08]
Largest Single-Period Moves[09]
- Mar 31, 2010102.20%
- Nov 5, 200311.19%
- Oct 1, 20084.83%
- Dec 17, 20034.51%
- Dec 29, 20043.89%
- Oct 5, 2005-3.84%
- Jan 1, 2025-3.76%
- Jul 3, 2002-3.52%
- Jun 4, 2003-3.06%
- Feb 28, 2007-2.90%
Calendar-Month Seasonality[10]
Average single-period return aggregated by the calendar month in which the period ended.
| MONTH | AVG RETURN | HIT % | N |
|---|---|---|---|
| January | -0.09% | 48.6% | 111 |
| February | -0.01% | 56.4% | 101 |
| March | 0.92% | 57.3% | 110 |
| April | 0.02% | 49.1% | 108 |
| May | 0.11% | 63.9% | 108 |
| June | -0.01% | 54.2% | 107 |
| July | 0.09% | 63.1% | 111 |
| August | 0.12% | 67.6% | 111 |
| September | 0.03% | 50.0% | 106 |
| October | 0.10% | 58.0% | 112 |
| November | 0.23% | 63.6% | 107 |
| December | 0.20% | 60.9% | 110 |
N = 1,303 OBS · GENERATED 2026-05-18 10:00Z
Forecast Approach
regime implied: The current macro regime classification (Goldilocks, Reflation, Stagflation, or Deflation) dictates the expected direction and magnitude of movement, calibrated against historical regime performance.
Key Drivers & Risks
- •Fed balance sheet
- •Bank reserves
- •Treasury General Account
- •Reverse repo facility
Historical Volatility
Low: trends are persistent, reversals are policy-driven
Frequently Asked Questions
What factors could push Credit Card Loans (Banks) higher?▾
The primary drivers that tend to lift Credit Card Loans (Banks) depend on the current macro regime. Financial conditions indexes are the Fed's dashboard. The Chicago Fed's NFCI blends over 100 inputs spanning equity volatility, credit spreads, funding stress, and leverage. Real yields across the TIPS curve reveal the true cost of capital after inflation, while liquidity measures (reverse repo, TGA, reserves) show whether the system is flush or stressed. Together they form the transmission belt from policy rate to real economy. Convex tracks these drivers live across the Liquidity category and flags when multiple forces align in the same direction. See the "Key Drivers & Risks" section on this page for the current list, and check the regime dashboard for how the macro backdrop is currently tilted.
What factors could push Credit Card Loans (Banks) lower?▾
The same transmission channels that drive Credit Card Loans (Banks) higher operate in reverse when conditions flip. The risk drivers listed above map directly to scenarios that, if triggered, would pull this metric in the opposite direction. Convex aggregates these into a scenario-weighted probability distribution rather than a point forecast, so the magnitude depends on which scenarios activate.
Where does consensus see Credit Card Loans (Banks) heading?▾
Rather than publish a point target that goes stale the day after release, Convex assembles consensus from the macro regime classification, active scenario probabilities, and historical base rates. Point forecasts from banks and strategists are worth reading for context, but they typically cluster around the consensus and miss the tail events that actually move markets. The scenario-weighted approach here captures that tail risk explicitly.
What is the historical range for Credit Card Loans (Banks)?▾
Historical ranges for Credit Card Loans (Banks) vary dramatically by regime. A level that is extreme in Goldilocks can be routine in Stagflation, and vice versa. The Historical Volatility section on this page describes the typical range and regime-specific behavior. For the full multi-decade history, visit the Credit Card Loans (Banks) chart page, which includes selectable time ranges up to five years and downloadable data.
How often is the Credit Card Loans (Banks) forecast updated?▾
This forecast page recalculates whenever the underlying data or regime classification changes, typically within hours of new data releases. The scenario probabilities refresh daily as the macro state is regenerated. Specific drivers listed on this page reflect the current state of the Convex regime engine, not static historical assumptions.
Is this forecast actionable for trading?▾
Convex forecasts are informational and educational. They describe probability distributions and regime-conditional paths rather than specific entry and exit levels. Traders and portfolio managers use them alongside other inputs including position sizing rules, risk management, and their own conviction calibration. They are not investment advice.
Get forecast updates for Credit Card Loans (Banks) and related indicators.
Forecasts are model-based projections derived from current regime classification, scenario probabilities, and historical patterns. They are not investment advice. All investments involve risk.