Based on current macro regime conditions and apple (aapl)'s historical behaviour in similar regimes, the model projects $321 by 2026-12-31 ( +6.8% from $300 today). The 68% confidence range is $256 to $385; the wider 95% range is $194 to $447. Methodology below the headline.
Apple (AAPL) Forecast 2026
Quantitative analysis from 1,298 observations of Apple (AAPL) history, joined to four universal macro regime classifications. Numbers are computed, not narrated.
Regime Scan[01/04]
Δ = divergence from +18.5% unconditional all-history average
Performance by Window[02]
| WINDOW | N | ANN RET | ANN VOL | RET/VOL | HIT % | TOTAL |
|---|---|---|---|---|---|---|
| 1Y | 262 | 44.13% | 22.18% | 1.99 | 51.7% | 43.80% |
| 3Y | 763 | 19.72% | 25.65% | 0.77 | 53.7% | 71.51% |
| 5Y | 1,268 | 18.92% | 27.33% | 0.69 | 52.7% | 137.77% |
| 10Y | 1,298 | 18.52% | 27.26% | 0.68 | 52.7% | 138.47% |
| All | 1,298 | 18.52% | 27.26% | 0.68 | 52.7% | 138.47% |
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 Apple (AAPL) 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) | 261 | 0.93% | 4.57% | 16.89% | 19.67% | 88.1% |
| Normal (15-25) | 842 | 2.49% | 7.69% | 12.75% | 11.43% | 82.5% |
| Elevated (25-40) | 176 | 2.35% | 1.13% | 15.15% | 17.39% | 78.8% |
| Extreme (>40) | 4 | n/a | n/a | n/a | n/a | n/a |
| REGIME BUCKET | N | +30D | +90D | +1Y AVG | +1Y MED | HIT % |
|---|---|---|---|---|---|---|
| Inverted (<0bps) | 540 | 2.34% | 8.14% | 18.65% | 20.19% | 91.9% |
| Flat (0-100bps) | 573 | 1.03% | 1.69% | 9.73% | 10.67% | 72.0% |
| Steep (>100bps) | 163 | 5.35% | 13.49% | 8.34% | 5.00% | 77.9% |
| REGIME BUCKET | N | +30D | +90D | +1Y AVG | +1Y MED | HIT % |
|---|---|---|---|---|---|---|
| Tight (<350bps) | 762 | 2.72% | 7.80% | 9.43% | 9.71% | 77.1% |
| Normal (350-500bps) | 469 | 0.56% | 3.88% | 18.20% | 20.16% | 88.3% |
| Stressed (>500bps) | 53 | 8.99% | 5.83% | 25.11% | 27.45% | 100.0% |
| REGIME BUCKET | N | +30D | +90D | +1Y AVG | +1Y MED | HIT % |
|---|---|---|---|---|---|---|
| Weak (bottom tercile) | 115 | 3.38% | 14.20% | 13.62% | 14.77% | 96.8% |
| Neutral (middle) | 336 | 3.99% | 8.11% | 2.17% | 0.63% | 52.8% |
| Strong (top tercile) | 818 | 1.25% | 4.86% | 16.61% | 16.57% | 88.3% |
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 Apple (AAPL); negative means it lags.
| ANCHOR | ROLE | PEAK LAG | PEAK CORR | ZERO-LAG | RELATIONSHIP |
|---|---|---|---|---|---|
| VIX | Volatility leader | 0d | -0.544 | -0.544 | coincident |
| HY OAS Spread | Credit risk leader | 0d | -0.393 | -0.393 | coincident |
| Trade-Weighted Dollar | FX driver | 0d | -0.165 | -0.165 | coincident |
| Copper | Global growth proxy | 0d | 0.124 | 0.124 | weak |
| Baa-10Y Spread | Credit risk (slow) | 0d | -0.118 | -0.118 | weak |
| 10Y Treasury Yield | Discount-rate driver | +41d | -0.095 | -0.031 | weak |
| NFCI | Financial conditions | +4d | -0.095 | -0.090 | weak |
| 10Y-2Y Yield Spread | Recession leader | -39d | 0.083 | -0.025 | weak |
| Initial Jobless Claims | Labor leader | +4d | -0.058 | -0.021 | 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 Apple (AAPL) 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 |
|---|---|---|---|---|
| Mar 7, 2025 | 239.0700 | -19.20% | -12.15% | 9.10% |
| Dec 6, 2024 | 242.8400 | -7.90% | -20.46% | 14.80% |
| Sep 6, 2024 | 220.8200 | 6.42% | 3.37% | 2.70% |
| Jun 7, 2024 | 196.8900 | 14.28% | 17.72% | 0.96% |
| Jan 26, 2024 | 192.4200 | -10.22% | 1.79% | 24.39% |
Worst Historical Drawdown[07]
Cross-Asset Correlations · 1Y[08]
Largest Single-Period Moves[09]
- Apr 9, 202515.33%
- Nov 10, 20228.90%
- Oct 28, 20227.56%
- Jun 11, 20247.26%
- Jan 28, 20226.98%
- Apr 3, 2025-9.25%
- Apr 4, 2025-7.29%
- Sep 13, 2022-5.87%
- May 18, 2022-5.64%
- May 5, 2022-5.57%
Calendar-Month Seasonality[10]
Average single-period return aggregated by the calendar month in which the period ended.
| MONTH | AVG RETURN | HIT % | N |
|---|---|---|---|
| January | -0.04% | 45.5% | 101 |
| February | -0.00% | 49.0% | 96 |
| March | 0.00% | 51.4% | 109 |
| April | 0.02% | 48.4% | 128 |
| May | 0.10% | 51.2% | 123 |
| June | 0.22% | 59.2% | 103 |
| July | 0.31% | 63.8% | 105 |
| August | 0.11% | 48.6% | 111 |
| September | -0.16% | 51.5% | 103 |
| October | 0.18% | 58.2% | 110 |
| November | 0.26% | 57.8% | 102 |
| December | -0.01% | 49.1% | 106 |
N = 1,298 OBS · GENERATED 2026-05-17 18:00Z
Forecast Approach
scenario weighted: We aggregate probability-weighted outcomes across active tracked scenarios, each with historical base rates and current heat scores. The projection above is the sample-weighted central estimate across current macro regime anchors; the scenario list below adds qualitative context.
Key Drivers & Risks
- •Company earnings
- •Sector dynamics
- •Macro environment
- •Valuation
Historical Volatility
High: individual stock vol exceeds index vol
How AAPL Forecasts Have Held Up Historically
Apple forecasts have a better track record than most single stocks because the company's earnings cadence is highly predictable: services revenue grows on subscription compounding, hardware revenue cycles with iPhone releases, and capital returns are formulaic. Sell-side AAPL price targets have median absolute miss of roughly 10% on a 12-month horizon, materially better than the 15-20% miss for the average S&P 500 single name.
Regime-conditional models on AAPL perform near 65% directional accuracy on monthly windows, lower than SPY because single-stock idiosyncratic risk (China demand, Vision Pro reception, AI strategy execution) is not in any macro classifier. The 2022 drawdown (-31%) and the 2024 China-headwinds episode were the two largest recent misses.
Regime Sensitivity for AAPL
AAPL is the lowest-beta of the Magnificent Seven, with realized correlation to QQQ around 0.75 and to SPY around 0.80. Goldilocks regimes map to forward 252-day returns averaging +12% (modestly above SPY); stagflation maps to roughly -5%; reflation near +8%; deflation near -8%.
The April 2026 setup is mixed for AAPL: 10Y at 4.31% is above the 2010s average and weighs on the multiple, but iPhone 16 Pro and the AI strategy reception have stabilized. China revenue (~17% of total) is the swing factor in either direction; a thaw in US-China trade tensions is bullish, escalation is bearish. The regime conditional reads as constructive but with a wider 68% band than usual because of the China and AI overhangs.
What Drives AAPL Forecast Errors
Three issues drive AAPL forecast errors. First, the China revenue line is a binary regime: trade tensions, regulatory action, or competitive pressure from Huawei produces step-changes that no macro classifier captures. The 2024 China headwinds episode took AAPL from ATH to a 17% drawdown without any change in the broader regime read.
Second, services revenue mix-shift compresses or expands the multiple in ways the regime model under-weights. Services revenue is now near 25% of total and trades at 35-40x earnings versus hardware near 20x. Mix-shift toward services is structurally multiple-supportive but rate-sensitive.
Third, capital returns timing is regime-dependent. AAPL has bought back roughly $700B in stock since 2012; the buyback cadence picks up in equity drawdowns and decelerates in rallies, providing a price floor that the bootstrap distribution under-states.
How to Use This Forecast in Practice
For AAPL, supplement the regime conditional with two structural overlays. First, watch China revenue per Q (reported in earnings) for binary regime shifts. Second, watch the services revenue growth rate; sustained 12%+ growth supports the multiple, deceleration below 8% would compress it.
The cleanest single relative-value signal for AAPL is its forward P/E versus the S&P 500. The historical median premium is roughly 1.5-2 P/E turns; readings above 5 turns flag stretched valuation, below 1 turn flag relative-value entry. The 68% band on AAPL should be treated as roughly 80% of QQQ's band because of AAPL's lower beta.
Frequently Asked Questions
What factors could push Apple (AAPL) higher?▾
The primary drivers that tend to lift Apple (AAPL) depend on the current macro regime. Apple Inc., the world's most valuable company by market cap. Convex tracks these drivers live across the Equity Stock 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 Apple (AAPL) lower?▾
The same transmission channels that drive Apple (AAPL) 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 Apple (AAPL) 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 Apple (AAPL)?▾
Historical ranges for Apple (AAPL) 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 Apple (AAPL) chart page, which includes selectable time ranges up to five years and downloadable data.
How often is the Apple (AAPL) 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.
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Forecasts are model-based projections derived from current regime classification, scenario probabilities, and historical patterns. They are not investment advice. All investments involve risk.