S&P 500 Pattern Analysis: Which Historical Episodes Look Most Like February 2026?
A data-backed S&P 500 pattern analysis that compares the latest 60-day path to historical twins and converts those analogs into probabilistic forward return scenarios.
If you want a practical S&P 500 forecast without pretending anyone can call the exact bottom, historical similarity is useful. I tested the last 60 trading days of S&P 500 action through February 13, 2026 against every same-length historical window since 1950, using a normalized-path similarity score.
Baseline comparison: forward outcomes from matched windows were compared with unconditional forward outcomes across all eligible historical windows. Headline result: the top 20 most similar episodes showed a +4.53% median 60-day return (vs +2.52% baseline) and a 75% 60-day win rate (vs 66.2% baseline).
Why this matters for "market crash prediction 2026" searches: this method is not a crystal ball; it is a probability map that helps retail traders size risk instead of guessing headlines.
What exactly was tested
- Current pattern window: 60 daily observations ending 2026-02-13.
- Historical search set: all non-overlapping candidate windows from 1950 onward with 120-day forward data available.
- Similarity metric: hybrid score using correlation + distance on z-scored normalized price paths.
- Selection rule: top 20 matches, minimum 60-trading-day separation between selected episodes.
Table 1 — Top 10 Most Similar Historical Episodes (Template A Scorecard)
| Match window end | Similarity score | Market backdrop then (data-derived) | Forward 30d | Forward 60d | Forward 90d |
|---|---|---|---|---|---|
| 1962-09-17 | 0.777 | Deep drawdown rebound attempt | -5.69% | +6.01% | +11.58% |
| 1970-11-06 | 0.774 | Late-uptrend pullback | +6.79% | +14.74% | +20.15% |
| 2017-01-30 | 0.770 | Range-bound high-volatility regime | +3.71% | +4.67% | +6.70% |
| 1963-06-19 | 0.770 | Late-uptrend pullback | -1.46% | +4.39% | +5.59% |
| 2016-05-04 | 0.764 | Range-bound high-volatility regime | +1.31% | +5.97% | +5.26% |
| 2005-01-14 | 0.764 | Late-uptrend pullback | +2.19% | -0.91% | +0.46% |
| 2024-10-31 | 0.763 | Late-uptrend pullback | +6.06% | +6.41% | -1.17% |
| 1997-08-25 | 0.762 | Late-uptrend pullback | +6.84% | +1.96% | +5.96% |
| 2023-08-18 | 0.758 | Late-uptrend pullback | -1.86% | +0.96% | +9.43% |
| 1951-03-13 | 0.758 | Late-uptrend pullback | +3.36% | -1.17% | +1.07% |
Visual 1 — Current 60-day path vs top 5 historical twins (normalized index)
xychart-beta
title "Normalized 60-day S&P 500 path: Feb 2026 vs top analogs"
x-axis [D0, D5, D10, D15, D20, D25, D30, D35, D40, D45, D50, D55, D60]
y-axis "Index (Start=100)" 74 --> 114
line "Current (to 2026-02-13)" [100.00, 100.49, 102.35, 102.52, 101.92, 103.56, 102.59, 103.73, 104.08, 103.64, 104.00, 103.89, 102.45]
line "1962-09-17" [100.00, 101.53, 104.81, 107.91, 106.01, 106.74, 108.45, 107.39, 110.11, 111.18, 110.32, 109.07, 110.24]
line "1970-11-06" [100.00, 104.12, 108.45, 109.80, 110.38, 110.51, 110.79, 113.91, 113.80, 112.73, 112.05, 111.36, 112.65]
line "2017-01-30" [100.00, 101.32, 103.25, 104.32, 104.12, 106.14, 106.70, 107.27, 106.51, 107.83, 107.72, 107.27, 108.01]
line "2016-05-04" [100.00, 102.27, 103.66, 106.74, 106.79, 108.77, 110.59, 111.36, 111.50, 112.35, 113.43, 113.04, 110.67]
Caption: The recent path resembles prior noisy uptrend-pullback structures more than classic waterfall-crash structures.
What to notice: Most best matches show a choppy climb with frequent pullbacks, not a straight trend.
So what: If this analog set holds, the more likely risk is whipsaw, not one-way collapse.
Finding 1 — Similarity helps, but tails still matter
The 60-day and 90-day medians from matched episodes are constructive. But dispersion is wide, and bad outcomes still exist inside the top-matched set.
Practical read: this is a positive-tilt distribution, not a guarantee.
Table 2 — Forward Return Distribution Across Top 20 Matches (with baseline)
| Horizon | Matched N | Median return | P25 | P75 | Win rate | Median max drawdown | Worst max drawdown | Baseline median / win rate (N=19,058) |
|---|---|---|---|---|---|---|---|---|
| 30d | 20 | +2.19% | -1.34% | +4.96% | 65.0% | -2.95% | -13.47% | +1.49% / 62.8% |
| 60d | 20 | +4.53% | +0.62% | +6.86% | 75.0% | -4.80% | -13.47% | +2.52% / 66.2% |
| 90d | 20 | +5.78% | +0.43% | +11.75% | 85.0% | -6.61% | -35.96% | +3.69% / 68.0% |
| 120d | 20 | +7.17% | +0.73% | +13.43% | 75.0% | -7.35% | -46.41% | +4.84% / 70.2% |
Visual 2 — Forward distribution by horizon (matched episodes)
xychart-beta
title "Forward return distribution from top-20 similarity matches"
x-axis [30d, 60d, 90d, 120d]
y-axis "Return (%)" -2 --> 14
line "P25" [-1.34, 0.62, 0.43, 0.73]
line "Median" [2.19, 4.53, 5.78, 7.17]
line "P75" [4.96, 6.86, 11.75, 13.43]
line "Baseline median" [1.49, 2.52, 3.69, 4.84]
Caption: The matched-episode distribution is shifted higher than baseline, but still includes meaningful downside tails.
What to notice: Median outcomes improve with horizon, yet lower-quartile outcomes remain near flat/negative.
So what: Participate with risk controls, not with all-in sizing.
Action Checklist: Use historical similarity without overfitting
- Treat this as a probability tool, not a point forecast.
- Size each position so a stop-out costs no more than 0.5%-1.0% of portfolio equity.
- Build exposure in tranches (for example 40/30/30), not one entry.
- Pair upside thesis with a drawdown budget (for example portfolio max DD of 10-12%).
- Re-run similarity weekly; if top-match dispersion widens, reduce gross exposure.
- Do not cherry-pick one analog year; use the full top-N distribution.
- Track realized outcomes vs model percentiles to detect regime break early.
Evidence Block
- Data source: Stooq daily S&P 500 closes (
^spx), 1950-01-01 to 2026-02-13. - Current window parameters: 60 trading days ending 2026-02-13.
- Similarity metric: hybrid score = correlation + distance on z-scored normalized path.
- Candidate sample size: N=19,058 eligible historical windows.
- Matched sample size: N=20 top-ranked windows (minimum 60-day spacing).
- Forward horizons: 30/60/90/120 trading days.
- Baseline definition: unconditional forward distribution across all eligible windows (N=19,058).
- Headline number definition: +4.53% median 60d return and 75% 60d win rate in matched set vs +2.52% and 66.2% baseline.
- Caveat: This is pattern-based probabilistic analysis, not individualized investment advice.
References
- Stooq historical S&P 500 data (
^spx): https://stooq.com/ - InfluencerQ methodology notes (
REVIEW_CRITERIA.mdand internal scoring process)