NAV 375.32 · June 30, 2026
A dynamic global equity strategy harnessing entropy signals to time allocation across world equity markets — capturing upside momentum while detecting regime shifts early.
Key Metrics
As of June 30, 2026 · Net of fees · Base 100 at inception (November 06, 2018)
Returns
Annual and monthly breakdown — 2026 year-to-date through June 30
| Metric | Since Inception | Last 12 Months |
|---|---|---|
| Total Net Return | +275.32% | +12.32% |
| Annualised Return | +18.17% | — |
| Annualised Volatility | 10.14% | 7.76% |
| Sharpe Ratio | 1.79 | 1.54 |
| Calmar Ratio | 1.45 | — |
| % Positive Months | 69% (63/91) | — |
| Latest NAV (Base 100) | 375.32 | — |
| Strategy Inception | November 06, 2018 | — |
| Report Date | June 30, 2026 | — |
Allocation
As of June 30, 2026 · Notional weights by asset class · Positive = long · Negative = short / hedge
Market Intelligence
As of June 30, 2026 · Score 0 = Maximum Calm · Score 1 = Maximum Stress · Portfolio average: 37.4%
Research
Why information-theoretic risk measures outperform standard deviation in global equity management
Standard deviation assumes Gaussian returns and symmetric risk — both systematically violated in equity markets.
First formalised by Claude Shannon (1948), entropy captures the full informational complexity of any distribution:
| Feature | Traditional Equity Strategies | EntropiaTech Entropy Approach |
|---|---|---|
| Risk Measure | Beta / standard deviation | Entropy (full distributional complexity) |
| Allocation Logic | Market-cap weighted / factor | Entropy-weighted, regime-adaptive |
| Regime Detection | Lagging — reacts to realised vol | Leading — entropy flags stress early |
| Tail Risk | Systematically underweighted | Naturally captured via distributional shape |
| Drawdown Management | Passive / discretionary | Entropy-triggered dynamic reduction |
| Overlay Triggers | None / ad hoc | Per-asset entropy score thresholds |