Qimtav analyzes real-time market data and economic indicators, and links each recommendation to a public scorecard that can be reviewed before making a decision.
Qimtav relies on predictive models trained on historical and real-time data, and produces recommendations with a clear confidence score rather than absolute numbers. The goal is to reduce reliance on guesswork in long-term financial decisions.
Each recommendation goes through a subsequent validation phase, and its outcome is recorded in a public scorecard available for review, allowing the methodology to be evaluated rather than relying on generic promises.
Three processing layers work sequentially: predictive modeling, real-time processing, and matching to the user's personal goals.
The system processes time series of prices and economic indicators to generate probability ranges for future outcomes, rather than a single fixed number. Each recommendation is accompanied by a confidence score that shows how stable the model is at that moment.
Inputs are constantly updated with each market impact change, minimizing the time lag between a change occurring and its reflection in a recommendation. This limits exposure to decisions based on delayed data.
Modeling results are linked to user data: time horizon, risk tolerance, and investment objective. The final recommendation reflects these data rather than a uniform general result for all users.
The table below shows the published log format. The complete record includes all recommendations issued and the date each result was verified.
| Period | Recommendation type | The result achieved | Verification status |
|---|---|---|---|
| First quarter 2024 | Asset allocation | Within the expected range | Verified |
| Second quarter 2024 | Portfolio rebalancing | Higher than the expected minimum | Verified |
| Third quarter 2024 | Volatility alert | Identical to the high risk scenario | Verified |
| Fourth quarter 2024 | Sectoral diversification | Under final review | Under review |
The values above are an illustrative example of the published data format. The complete history includes every recommendation issued by the system with verification details.
A consistent sequence keeps it clear where each recommendation comes from and why.
The system collects market data, economic indicators and the user's account data from pre-defined sources.
Predictive models combine this data to generate potential scenarios, and accompany each scenario with an estimated risk ratio.
The user gets a prioritized recommendation, with a brief explanation of the factors that influenced the score.
Four independent units work together to protect the financial decision from unconsidered fluctuations.
Real-time monitoring of unusual market movements, and immediate notification when pre-set user limits are exceeded.
A proposed reallocation of assets based on the target risk ratio and the specified investment time horizon.
Measuring the general trend from multiple text data sources to estimate buying or selling pressure on a specific asset.
Identifying excessive concentration in a single sector or asset, and proposing alternatives to reduce correlation between portfolio components.
Account data is stored on encrypted servers and is not shared with third parties for marketing purposes. The user can request deletion of his data at any time by contacting the support team.
Each recommendation is compared to the actual outcome after a pre-determined period of time, and is then classified as “achieved” or “under review” in the public record described in the Scorecard section.
The subscription level determines the scope of access to the different analysis modules. Details of levels and prices are available upon direct contact with the sales team.
You can review the format of published data first, or start a live analysis of your financial situation.