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S&P 500

Equity index · Monthly series · IX-SPX

Latest, October 2026

7,580.1 pts

-0.9% from September 2026

Open interactive chartS&P 500 in the app

992660622199November 2016October 2027
History through October 2026, the forecast path to October 2027 (dashed) and its 80% interval (shaded).

Forecast

Forecast for October 2027: 8,334.0 pts, with an 80% interval of 6,742.4 pts to 9,925.6 pts.

Forecast path. Intervals are shown where available.
PeriodForecast80% low80% high
November 20267,717.8 pts7,126.0 pts8,309.6 pts
December 20267,723.1 pts7,465.3 pts7,981.0 pts
January 20277,761.3 pts7,100.3 pts8,422.2 pts
February 20277,790.7 pts7,613.0 pts7,968.4 pts
March 20277,839.0 pts6,995.0 pts8,683.1 pts
April 20278,003.5 pts7,403.2 pts8,603.8 pts
May 20278,160.3 pts7,166.5 pts9,154.1 pts
June 20278,152.7 pts7,345.1 pts8,960.4 pts
July 20278,192.5 pts7,186.5 pts9,198.5 pts
August 20278,223.0 pts7,659.6 pts8,786.5 pts
September 20278,254.0 pts7,280.4 pts9,227.7 pts
October 20278,334.0 pts6,742.4 pts9,925.6 pts

Model: Gradient-boosted trees (machine learning). Forecast generated 2026-11-14.

AI confidence

Limited evidence

At least 6 complete tests are required; 5 are available.

Model: Gradient-boosted trees (machine learning)

Accuracy vs naive
n/aToo few tests (5 tests, 6 needed) (5 tests, 4 won)
Direction
n/aToo few tests (5 tests, 6 needed) (44 of 60 calls right in 5 tests)
Turning points
n/aToo few tests (5 tests, 6 needed) (28 turns in 5 tests, 0 without a turn)
Error vs baseline
15% lowerError ratio 0.85; 1.00 is a tie.
Tests won
4 / 5Complete recent tests excluded from model selection; ties count as no win.
Test period
2021-11-01 to 2026-10-0112 observations ahead per test.
80% band coverage
88%Observed outcomes inside the band across all stored test windows.
How this assessment is made

AI confidence describes historical evidence for this forecast, not a probability of being correct. Each backtest fits on data available before its test window. The baseline carries the last value forward. Supported evidence requires at least six complete, non-overlapping tests, an error ratio below 0.90 (rounded to two decimals), and wins in a majority of tests. Ratios from 0.90 to below 1.10 are on par; 1.10 or above is weak evidence. Inconsistent wins produce mixed evidence. For a selected model, only the recent tests excluded from model selection determine this label. These are historical simulations using the available data, not a live trading track record; revisions and foundation-model pretraining can limit how closely they reproduce a past decision. Past performance can change.

Replay past tests

Model comparison

5 of 5 models evaluatedModels are selected on older tests and evaluated on separate recent tests. All four classes completed their backtests.

Models tested

Gradient-boosted trees scored lowest on the 2 older tests (0.78) and was chosen. The 5 recent tests played no part in the choice and give its grade (0.85). The search for a model that beats the naive forecast on both sets of tests goes on. Scores are MASE: a model's errors divided by the naive forecast's, so 1 is a tie.

4 model classes completed the tests: Machine learning, Neural network, Foundation model, Statistical.

Every model tested on this series.
ModelClass and computeOlder testsRecent testsRole
CatBoost (machine learning)Machine learning · CPU0.780.85Serving
N-HiTS (neural network)Neural network · CPU0.870.97Tested
Timestral (foundation model)Foundation model · CPU0.930.91Tested
AutoETS (statistical)Statistical · CPU0.950.82Tested
ci-proto (foundation model)Foundation model · CPU0.950.91Tested
Naive forecast (last value held)Benchmark · CPU1.001.00Benchmark

Uses ci-proto, released by Continuous Intelligence. Continuous Intelligence does not endorse or approve this product or service. (ci-proto is shown as State-space model, 167M.)

Reading

It is above 98% of the 119 earlier readings since November 2016.

Written from the figures by a fixed template; no AI model is involved.

Recent history (24 observations)
The 24 most recent observations, newest first.
PeriodValue
October 20267,580.1 pts
September 20267,651.5 pts
August 20267,686.1 pts
July 20267,489.7 pts
June 20267,499.4 pts
May 20267,580.1 pts
April 20267,209.0 pts
March 20266,528.5 pts
February 20266,878.9 pts
January 20266,939.0 pts
December 20256,845.5 pts
November 20256,849.1 pts
October 20256,840.2 pts
September 20256,688.5 pts
August 20256,460.2 pts
July 20256,339.4 pts
June 20256,204.9 pts
May 20255,911.7 pts
April 20255,569.1 pts
March 20255,611.8 pts
February 20255,954.5 pts
January 20256,040.5 pts
December 20245,881.6 pts
November 20246,032.4 pts

Source

Data: public market data feeds.

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