01 /
"Earnings multiples look cheap - conviction is high."
Over-anchoring on valuation without testing against current market structure or portfolio-level impact.
GanIQ evaluates every capital allocation decision against a model-driven benchmark, quantifying its impact on the portfolio before execution.
Not a prediction.
A system that ensures every decision stands up to scrutiny before capital moves.
GanIQ brings that rigor into every capital allocation decision, applying systematic validation before money moves.
"Earnings multiples look cheap - conviction is high."
Over-anchoring on valuation without testing against current market structure or portfolio-level impact.
"Consensus agrees. Six analysts have a Buy rating."
Analyst consensus is built on assumptions that are often six months stale - and applied generically across all portfolios.
"The risk framework was applied. Position is sized."
Sizing decisions made against models that ignore what each decision does to the portfolio as a whole. Not incompetence - a structural gap.
Is this decision statistically defensible for this portfolio?
Once cleared - how much of their capital should be behind it?
And does it quietly overexpose the portfolio to one outcome?
The engine doesn't apply fixed rules to market data. It continuously interprets market behaviour - reading how conditions are moving across trend, volatility, and participation - and adapts its verdict on each decision accordingly.
When conditions shift, the engine shifts. The same trade that was defensible last week may not be defensible today. That's what separates an adaptive engine from a static model.
And because it runs against the actual portfolio - not a generic benchmark - no two portfolios receive the same output. That personalisation is the core of the moat.
Once the ML engine returns its verdict, GanIQ determines how much of the portfolio should be behind it - based on the expected edge and the current state of the book. Conviction is not the answer to that question. Putting too much behind a winning thesis and too little behind a cautious one both erode returns in ways that don't show up until it's too late.
Once the decision is weighted, GanIQ checks whether it makes the portfolio more vulnerable to a single sector, theme, or event than it appears. This kind of exposure doesn't announce itself. It builds through a series of individually reasonable decisions. This layer flags it before capital is deployed.
Confirmed or flagged - with full sizing and exposure context. Every override logged. Every decision accountable.
that need an edge
Most portfolios rely on valuations and consensus - while how much capital is behind each decision, and whether the portfolio is quietly overexposed to one outcome, go unchecked.
GanIQ adds the missing layer. It stress-tests every decision against real market behaviour before execution.
The edge isn't fundamental vs quant. It's getting the balance right.
| Without GanIQ | With GanIQ | |
|---|---|---|
| Pre-trade validation | Gut conviction, analyst consensus - no independent check on the decision itself | An adaptive ML engine reads current market conditions and returns a verdict on whether the decision is defensible right now - for this portfolio |
| Decision context | Generic signals - same output for every portfolio | Personalised to the exact portfolio being traded - different portfolios, different verdicts |
| Capital behind each call | Driven by conviction - too much or too little goes unnoticed | Determined after ML verdict - conviction is not the input |
| Hidden exposure | Invisible until a drawdown reveals it | Checked after ML clears the decision - flagged before capital moves |
| Investor visibility | Periodic reporting, after the fact | Real-time, independent validation layer - every decision logged |
Doubt is the exit trigger. Visible proof removes it.
Live signals create a reason to return daily.
Every decision backed by a visible, independent validation layer.
Signal-clear output: hold, trim, add, rebalance.
Stickiness built on compounding value - not inertia.
Quant intelligence as a native feature, not a bolt-on.
Apply for early access. Start with your clients' portfolios - audit the last 12 months of decisions and pinpoint where execution broke down.
No payment. No commitment.
Real-time validation before every execution. Reserved for beta users first - apply now to hold your place in the queue.
This is not another analytics dashboard. This is a decision engine your clients can see working on their own capital.
Behind every underperforming portfolio is a series of decisions that were never independently validated. GanIQ closes that gap - before execution, not after.
Illustrative report layout only. Portfolio name and reference are anonymised. Figures are representative, not a forecast, guarantee, or promise of performance.
Using Conservative (~100 stocks) · Four parallel runs