Alibaba Health Information Technology Ltd.

Equity · Monthly series · EQ-241.HKEX

Latest, October 2026

2.83 HKD

-1.4% from September 2026

Open interactive chartAlibaba Health Information Technology Ltd. in the app

2613.20.4064November 2016October 2027
History through October 2026, the forecast path to October 2027 (dashed) and its 80% interval (shaded).

Forecast

Forecast for October 2027: 2.59 HKD, with an 80% interval of 0.41 HKD to 7.53 HKD.

Forecast path. Intervals are shown where available.
PeriodForecast80% low80% high
November 20262.79 HKD2.18 HKD3.69 HKD
December 20262.73 HKD2.23 HKD3.56 HKD
January 20272.71 HKD1.66 HKD4.61 HKD
February 20272.68 HKD1.48 HKD4.94 HKD
March 20272.66 HKD1.39 HKD5.18 HKD
April 20272.64 HKD1.08 HKD5.84 HKD
May 20272.63 HKD1.24 HKD5.62 HKD
June 20272.63 HKD1.12 HKD5.93 HKD
July 20272.62 HKD0.47 HKD7.35 HKD
August 20272.60 HKD1.04 HKD6.09 HKD
September 20272.60 HKD0.78 HKD6.73 HKD
October 20272.59 HKD0.41 HKD7.53 HKD

Model: Agentic Investor TSFM (foundation model). Forecast generated 2026-11-12.

AI confidence

Needs refresh

The data refresh is due. Confidence will be reassessed against the refreshed history.

Model: Agentic Investor TSFM (foundation model)

Accuracy vs naive
n/aNot evaluated
Direction
n/aNot evaluated
Turning points
n/aNot evaluated
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.

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

Agentic Investor TSFM scored lowest on the 6 older tests (1.11) and was chosen. The 6 recent tests played no part in the choice and give its grade (0.91). 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: Foundation model, Machine learning, Statistical, Neural network.

Every model tested on this series.
ModelClass and computeOlder testsRecent testsRole
Naive forecast (last value held)Benchmark · CPU1.001.00Benchmark
Timestral (foundation model)Foundation model · CPU1.110.91Serving
ci-proto (foundation model)Foundation model · CPU1.220.88Tested
CatBoost (machine learning)Machine learning · CPU1.231.05Tested
AutoETS (statistical)Statistical · CPU1.301.88Tested
N-HiTS (neural network)Neural network · CPU1.363.17Tested

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

That is the lowest of the 120 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 20262.83 HKD
September 20262.87 HKD
August 20263.09 HKD
July 20263.59 HKD
June 20263.08 HKD
May 20263.63 HKD
April 20264.37 HKD
March 20264.66 HKD
February 20265.58 HKD
January 20266.52 HKD
December 20255.05 HKD
November 20255.57 HKD
October 20255.80 HKD
September 20256.66 HKD
August 20255.46 HKD
July 20254.88 HKD
June 20254.74 HKD
May 20254.53 HKD
April 20255.04 HKD
March 20254.71 HKD
February 20255.01 HKD
January 20253.61 HKD
December 20243.32 HKD
November 20243.65 HKD

Source

Data: public market data feeds.

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