Based on current macro regime conditions and semiconductors (smh)'s historical behaviour in similar regimes, the model projects $654 by 2026-12-31 ( +17.6% from $556 today). The 68% confidence range is $503 to $806; the wider 95% range is $357 to $951. Methodology below the headline.
Semiconductors (SMH) Forecast 2026
Quantitative analysis from 2,116 observations of Semiconductors (SMH) history, joined to four universal macro regime classifications. Numbers are computed, not narrated.
Regime Scan[01/04]
Δ = divergence from +33.3% unconditional all-history average
Performance by Window[02]
| WINDOW | N | ANN RET | ANN VOL | RET/VOL | HIT % | TOTAL |
|---|---|---|---|---|---|---|
| 1Y | 262 | 127.29% | 29.98% | 4.25 | 57.9% | 126.15% |
| 3Y | 763 | 60.76% | 34.47% | 1.76 | 55.2% | 315.06% |
| 5Y | 1,268 | 36.98% | 34.81% | 1.06 | 53.3% | 382.22% |
| 10Y | 2,116 | 33.30% | 34.60% | 0.96 | 53.5% | 1008.58% |
| All | 2,116 | 33.30% | 34.60% | 0.96 | 53.5% | 1008.58% |
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 Semiconductors (SMH) 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) | 537 | 1.81% | 5.19% | 27.59% | 27.23% | 85.4% |
| Normal (15-25) | 1,199 | 3.31% | 12.16% | 32.45% | 38.52% | 75.3% |
| Elevated (25-40) | 326 | 7.73% | 15.22% | 45.66% | 49.97% | 88.1% |
| Extreme (>40) | 39 | 16.33% | 43.26% | 104.16% | 101.84% | 100.0% |
| REGIME BUCKET | N | +30D | +90D | +1Y AVG | +1Y MED | HIT % |
|---|---|---|---|---|---|---|
| Inverted (<0bps) | 543 | 4.78% | 16.31% | 44.88% | 49.34% | 92.8% |
| Flat (0-100bps) | 1,337 | 3.54% | 9.99% | 38.27% | 41.60% | 85.1% |
| Steep (>100bps) | 208 | 3.67% | 7.04% | -9.65% | -9.27% | 25.5% |
| REGIME BUCKET | N | +30D | +90D | +1Y AVG | +1Y MED | HIT % |
|---|---|---|---|---|---|---|
| Tight (<350bps) | 912 | 2.81% | 7.17% | 12.31% | 3.89% | 57.6% |
| Normal (350-500bps) | 970 | 3.40% | 11.39% | 42.97% | 45.18% | 92.4% |
| Stressed (>500bps) | 220 | 10.29% | 27.36% | 67.77% | 63.65% | 100.0% |
| REGIME BUCKET | N | +30D | +90D | +1Y AVG | +1Y MED | HIT % |
|---|---|---|---|---|---|---|
| Weak (bottom tercile) | 171 | 3.79% | 9.37% | 8.28% | 1.24% | 51.3% |
| Neutral (middle) | 480 | 5.84% | 11.42% | 1.10% | -6.98% | 39.0% |
| Strong (top tercile) | 1,422 | 3.23% | 11.60% | 44.35% | 46.88% | 92.2% |
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 Semiconductors (SMH); negative means it lags.
| ANCHOR | ROLE | PEAK LAG | PEAK CORR | ZERO-LAG | RELATIONSHIP |
|---|---|---|---|---|---|
| VIX | Volatility leader | 0d | -0.621 | -0.621 | coincident |
| HY OAS Spread | Credit risk leader | 0d | -0.480 | -0.480 | coincident |
| Trade-Weighted Dollar | FX driver | 0d | -0.258 | -0.258 | coincident |
| Copper | Global growth proxy | 0d | 0.249 | 0.249 | coincident |
| Baa-10Y Spread | Credit risk (slow) | 0d | -0.207 | -0.207 | coincident |
| 10Y Treasury Yield | Discount-rate driver | 0d | 0.169 | 0.169 | coincident |
| Initial Jobless Claims | Labor leader | -5d | -0.146 | 0.035 | weak |
| NFCI | Financial conditions | -4d | -0.140 | -0.008 | weak |
| 10Y-2Y Yield Spread | Recession leader | -3d | -0.090 | -0.018 | 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 Semiconductors (SMH) 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 |
|---|---|---|---|---|
| May 14, 2025 | 247.9900 | 12.27% | 29.72% | 110.77% |
| Feb 13, 2025 | 251.9200 | -15.78% | 9.24% | 61.76% |
| Nov 15, 2024 | 239.9500 | 0.93% | -11.87% | 41.46% |
| Aug 16, 2024 | 246.7100 | -0.51% | 1.89% | 17.13% |
| May 17, 2024 | 230.2500 | 14.50% | 9.72% | 4.87% |
Worst Historical Drawdown[07]
Cross-Asset Correlations · 1Y[08]
Largest Single-Period Moves[09]
- Apr 9, 202517.16%
- Mar 24, 202010.32%
- Nov 10, 202210.22%
- Apr 6, 20209.94%
- Mar 13, 20209.70%
- Mar 16, 2020-14.41%
- Mar 12, 2020-10.56%
- Jan 27, 2025-9.83%
- Apr 3, 2025-8.65%
- Mar 9, 2020-8.32%
Calendar-Month Seasonality[10]
Average single-period return aggregated by the calendar month in which the period ended.
| MONTH | AVG RETURN | HIT % | N |
|---|---|---|---|
| January | 0.24% | 58.8% | 182 |
| February | 0.12% | 52.9% | 172 |
| March | 0.00% | 49.5% | 196 |
| April | 0.12% | 50.3% | 193 |
| May | 0.32% | 53.5% | 187 |
| June | 0.20% | 57.2% | 166 |
| July | 0.23% | 55.9% | 170 |
| August | -0.01% | 52.5% | 177 |
| September | -0.07% | 54.0% | 163 |
| October | 0.06% | 52.2% | 178 |
| November | 0.43% | 57.7% | 163 |
| December | 0.02% | 48.8% | 168 |
N = 2,116 OBS · GENERATED 2026-05-17 17:30Z
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
- •Sector rotation
- •Earnings cycle
- •Rate sensitivity
- •Macro regime
Historical Volatility
Moderate-high: sector dispersion varies by cycle
Scenarios That Affect This Forecast
How SMH Forecasts Have Held Up Historically
Semiconductor sector forecasts have one of the most volatile track records of any sub-sector because SMH cycles between extreme over-supply (2018-2019, 2022-2023) and extreme under-supply (2020-2021, 2024-2026 AI-driven). Sell-side targets have missed by 25%+ in median absolute terms over 2010-2025; the 2022 SMH drawdown (-35%) and the 2023-2025 recovery (+200% cumulative) were both consensus misses.
Regime-conditional models on SMH achieve approximately 60% directional accuracy. Hyperscaler capex is the dominant input; secondary inputs are PC and smartphone demand cycles, automotive semis demand, and inventory drawdown signals.
Regime Sensitivity for SMH
SMH is the highest-beta major equity ETF to the AI-capex regime, with NVDA representing roughly 20% of the index, TSM, AVGO, AMD, and ASML adding another 35%. Goldilocks regimes map to forward 252-day SMH returns averaging +28%; stagflation near -15%; reflation near +18%; deflation near -22%. The amplification factor versus SPY is the largest of any sub-sector ETF.
The April 2026 setup has hyperscaler capex tracking at $300B+ annually, NVDA datacenter revenue exceeding $35B per quarter, and TSM 2nm capacity ramping. The regime conditional reads as constructive in any environment where AI capex sustains; downside risk is concentrated in any quarter where hyperscaler capex guidance disappoints.
What Drives SMH Forecast Errors
Three structural issues drive SMH forecast errors. First, the AI-capex cycle has no historical regime template. From November 2022 (ChatGPT launch) through 2025, semiconductor revenue has grown faster than at any point in the modern era, breaking historical regression relationships.
Second, NVDA single-name risk dominates the sector. A 5% NVDA move translates to 1% on SMH; the model treats sector composition as stable but NVDA's weight has grown from 5% in 2020 to 20% in 2026 as the stock outperformed.
Third, the China export-control regime is binary. Each escalation (Biden 2022, 2023, Trump 2.0 2025-2026) re-prices NVDA, AMD, AVGO data-center revenue assumptions that no macro classifier captures.
How to Use This Forecast in Practice
For SMH, watch the four hyperscalers' combined capex guidance per Q (the dominant input), TSM monthly revenue (the cleanest single industry-wide indicator), and SOX index breadth (semis-broader-than-NVDA participation).
The cleanest cross-check is the SMH-XLK spread. SMH leading XLK signals AI-hardware-cycle dominance; XLK leading signals broader software-and-services participation. The 68% band on SMH should be treated as 50%+ wider than the historical bootstrap implies because of the AI-capex regime change.
Frequently Asked Questions
What factors could push Semiconductors (SMH) higher?▾
The primary drivers that tend to lift Semiconductors (SMH) depend on the current macro regime. VanEck Semiconductor ETF, leads the tech cycle. Convex tracks these drivers live across the Equity Sector 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 Semiconductors (SMH) lower?▾
The same transmission channels that drive Semiconductors (SMH) 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 Semiconductors (SMH) 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 Semiconductors (SMH)?▾
Historical ranges for Semiconductors (SMH) 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 Semiconductors (SMH) chart page, which includes selectable time ranges up to five years and downloadable data.
How often is the Semiconductors (SMH) 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 Semiconductors (SMH) 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.