A hedged multi-sector global bond strategy combining IG, HY and Emerging Market debt with active CDS and CDX overlays — delivering consistent income with strict drawdown control.
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 | +140.15% | +2.22% |
| Annualised Return | +11.7% | — |
| Annualised Volatility | 4.82% | 2.48% |
| Sharpe Ratio | 2.42 | 0.97 |
| Calmar Ratio | 3.52 | — |
| % Positive Months | 68% (62/91) | — |
| Latest NAV (Base 100) | 240.15 | — |
| 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 bond management
Standard duration and spread metrics assume linear, symmetric risk — assumptions that systematically fail during credit stress and rate dislocations.
First formalised by Claude Shannon (1948), entropy captures the full informational complexity of any return distribution:
| Feature | Traditional Bond Strategies | EntropiaTech Entropy Approach |
|---|---|---|
| Risk Measure | Duration / OAS / VaR | Entropy (full distributional complexity) |
| Allocation Logic | Index tracking / static tilts | Entropy-weighted, regime-adaptive |
| Credit Risk Detection | Lagging — reacts to spread widening | Leading — entropy flags stress early |
| Tail Risk | Systematically underweighted | Naturally captured via distributional shape |
| Sector Rotation | Discretionary / benchmark-relative | Entropy-triggered dynamic reallocation |
| Overlay Triggers | None / rules-based stop-loss | Per-sector entropy score thresholds |