Vietnam Capital Flows

Vietnam · Quarterly series · EC-VN-CF

Latest, Q2 2026

1.90B USD

+112.7% from Q1 2026

Open interactive chartVietnam Capital Flows in the app

History through Q2 2026, the forecast path to Q2 2027 (dashed) and its 80% interval (shaded).

Forecast

Forecast for Q2 2027: 804.7M USD, with an 80% interval of -9.90B USD to 11.51B USD.

Forecast path. Intervals are shown where available.
Period	Forecast	80% low	80% high
Q3 2026	804.7M USD	-3.10B USD	4.71B USD
Q4 2026	804.7M USD	-10.26B USD	11.87B USD
Q1 2027	804.7M USD	-5.56B USD	7.17B USD
Q2 2027	804.7M USD	-9.90B USD	11.51B USD

Model: Exponential smoothing (statistical). Forecast generated 2026-11-14.

AI confidence

Needs refresh

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

Model: Exponential smoothing (statistical)

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

Exponential smoothing scored lowest on the 2 older tests (1.05) and was chosen. The 6 recent tests played no part in the choice and give its grade (0.84). 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: Statistical, Foundation model, Machine learning, Neural network. Previous model roster.

Every model tested on this series.
Model	Class and compute	Older tests	Recent tests	Role
Naive forecast (last value held)	Benchmark · CPU	1.00	1.00	Benchmark
Exponential smoothing (statistical)	Statistical · CPU	1.05	0.84	Serving
State-space model, 167M (foundation model)	Foundation model · CPU	1.10	0.79	Tested
Agentic Investor TSFM (foundation model)	Foundation model · CPU	1.10	0.82	Tested
Gradient-boosted trees (machine learning)	Machine learning · CPU	1.35	0.77	Tested
Hierarchical neural network (neural network)	Neural network · CPU	1.90	0.89	Tested

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 49% of the 39 earlier readings since Q3 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.
Period	Value
Q2 2026	1.90B USD
Q1 2026	893.0M USD
Q4 2025	7.08B USD
Q3 2025	-591.0M USD
Q2 2025	-4.80B USD
Q1 2025	-3.51B USD
Q4 2024	267.0M USD
Q3 2024	-2.73B USD
Q2 2024	-6.26B USD
Q1 2024	1.44B USD
Q4 2023	-1.64B USD
Q3 2023	-2.77B USD
Q2 2023	-2.72B USD
Q1 2023	3.35B USD
Q4 2022	2.10B USD
Q3 2022	-4.93B USD
Q2 2022	8.88B USD
Q1 2022	3.50B USD
Q4 2021	6.92B USD
Q3 2021	8.75B USD
Q2 2021	10.08B USD
Q1 2021	5.16B USD
Q4 2020	5.30B USD
Q3 2020	1.27B USD

Source

Data: public market data feeds.