AgenticInvestor

Singapore Bankruptcies

Singapore · Monthly series · EC-SG-BR

Latest, October 2026

136.0 pts

-18.1% from September 2026

Open interactive chartSingapore Bankruptcies in the app

286.2146.67November 2016October 2027
History through October 2026, the forecast path to October 2027 (dashed) and its 80% interval (shaded).

Forecast

Forecast for October 2027: 160.8 pts, with an 80% interval of 155.4 pts to 277.3 pts.

Forecast path. Intervals are shown where available.
PeriodForecast80% low80% high
November 2026151.4 pts125.1 pts177.5 pts
December 2026146.2 pts123.3 pts254.3 pts
January 2027176.6 pts141.0 pts203.3 pts
February 2027157.7 pts149.1 pts195.2 pts
March 2027167.1 pts149.2 pts184.2 pts
April 2027173.3 pts150.7 pts216.0 pts
May 2027160.5 pts138.0 pts194.7 pts
June 2027180.2 pts149.3 pts201.3 pts
July 2027180.9 pts148.1 pts207.2 pts
August 2027160.1 pts144.9 pts200.7 pts
September 2027180.6 pts23.8 pts286.2 pts
October 2027160.8 pts155.4 pts277.3 pts

Model: Hierarchical neural network (neural network). Forecast generated 2026-11-14.

AI confidence

Needs refresh

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

Model: Hierarchical neural network (neural network)

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

Previous comparisonThe stored comparison uses earlier data or a previous model roster. An eligible nightly batch can compare both candidates, subject to call budgets and retry limits.

Models tested

Hierarchical neural network scored lowest on the 6 older tests (0.68) and was chosen. The 6 recent tests played no part in the choice and give its grade (1.07). 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: Neural network, Statistical, Foundation model, Machine learning. Previous model roster.

Every model tested on this series.
ModelClass and computeOlder testsRecent testsRole
Hierarchical neural network (neural network)Neural network · CPU0.681.07Serving
Exponential smoothing (statistical)Statistical · CPU0.771.03Tested
State-space model, 167M (foundation model)Foundation model · CPU0.770.92Tested
Agentic Investor TSFM (foundation model)Foundation model · CPU0.840.91Tested
Naive forecast (last value held)Benchmark · CPU1.001.00Benchmark
Gradient-boosted trees (machine learning)Machine learning · CPU1.200.97Tested

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 67% 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 2026136.0 pts
September 2026166.0 pts
August 2026145.0 pts
July 2026168.0 pts
June 2026134.0 pts
May 2026157.0 pts
April 2026182.0 pts
March 2026142.0 pts
February 2026168.0 pts
January 2026172.0 pts
December 2025116.0 pts
November 2025112.0 pts
October 2025146.0 pts
September 2025147.0 pts
August 2025125.0 pts
July 2025144.0 pts
June 2025152.0 pts
May 2025148.0 pts
April 2025155.0 pts
March 2025140.0 pts
February 2025140.0 pts
January 202598.0 pts
December 2024132.0 pts
November 2024108.0 pts

Source

Data: public market data feeds.

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