EntropiaTech
Overview Performance Positioning Entropy Research
NAV 236.31  ·  June 30, 2026
EntropiaTech · Global Bonds

Fixed income alpha
through entropy-driven
bond allocation

A multi-sector global bond strategy spanning investment grade, high yield and emerging market debt — with entropy signals guiding dynamic allocation and credit risk management.

+136.31%
Total Return
2.34
Sharpe Ratio
-5.77%
Max Drawdown
NAV · BASE 100 · NOV 2018 – JUN 2026

Key Metrics

Strategy Overview

As of June 30, 2026  ·  Net of fees  ·  Base 100 at inception (November 06, 2018)

236.31
Latest NAV
Base 100
+136.31%
Total Return
Since inception
+11.47%
Ann. Return
Since inception
2.34
Sharpe Ratio
Since inception
-5.77%
Max Drawdown
March 04, 2020
4.9%
Ann. Volatility
Since inception
NAV Performance vs. Flat Benchmark
Net of fees — Base 100 on November 06, 2018
Drawdown History
Underwater curve — all episodes (UCITS peak-to-valley)
Rolling 12-Month Return
252 business-day rolling return, net of fees

Returns

Performance Analysis

Annual and monthly breakdown — 2026 year-to-date through June 30

Annual Net Returns
Net of fees — 2026 partial year (YTD to June 30)
Monthly Return Calendar (%)
Net of fees  ·  65/91 positive months (71%)  ·  Green = positive  ·  Red = negative
Metric Since InceptionLast 12 Months
Total Net Return+136.31%+6.43%
Annualised Return+11.47%—
Annualised Volatility4.9%2.04%
Sharpe Ratio2.343.24
Calmar Ratio1.99—
% Positive Months71% (65/91)—
Latest NAV (Base 100)236.31—
Strategy InceptionNovember 06, 2018—
Report DateJune 30, 2026—
Maximum Drawdown — UCITS Peak-to-Valley
MDD: -5.77%  ·  Peak: March 04, 2020  ·  Trough: March 20, 2020
Drawdown duration: 12 business days (16 cal. days)  ·  Recovery date: March 26, 2020
Time to recovery: 4 business days (6 cal. days)  ·  Full cycle: 16 business days (22 cal. days)
Key Ratios
Since inception vs. last 12 months

Allocation

Current Portfolio Exposure

As of June 30, 2026  ·  Notional weights by asset class  ·  Positive = long  ·  Negative = short / hedge

Bond Sector Exposures
Entropy-weighted allocation across IG, HY and Emerging Market debt
Exposure Detail
Notional weights as of report date

Market Intelligence

Entropia Scores by Asset Class

As of June 30, 2026  ·  Score 0 = Maximum Calm  ·  Score 1 = Maximum Stress  ·  Portfolio average: 37.4%

Entropia Score Ranking
Green < 0.33 (Calm)  ·  Orange 0.33–0.66 (Neutral)  ·  Red ≥ 0.66 (Stress)

Research

Scientific Foundation: Entropy vs. Volatility

Why information-theoretic risk measures outperform standard deviation in global bond management

The Limits of Volatility in Fixed Income

Standard duration and spread metrics assume linear, symmetric risk — assumptions that systematically fail during credit stress and rate dislocations.

  • Non-normality: Credit spread returns exhibit fat tails and negative skewness (Mandelbrot, 1963) — standard deviation understates the risk.
  • Asymmetry blindness: Variance penalises upside and downside equally — inconsistent with convex bond return profiles and investor loss aversion (Kahneman & Tversky, 1979).
  • Backward-looking: Realised vol rises after a credit event. Entropy detects distributional stress early (Gradojevic & Caric, 2017).
  • Regime-blind: A single spread metric cannot distinguish a carry-driven compression from a pre-crisis tightening with similar vol but very different risk profiles.

Shannon Entropy as Risk Measure

First formalised by Claude Shannon (1948), entropy captures the full informational complexity of any return distribution:

H(X) = − Σ pᵢ · log₂(pᵢ)
  • Distribution-free: Valid under fat tails, bimodality and regime switches — no Gaussian assumption (Scrucca, 2024).
  • Tail-sensitive: Rényi entropy calibrates explicitly to tail behaviour (Lassance & Vrins, 2019).
  • Early warning: Identified every major credit crisis 1998–2026: GFC, COVID, 2022 rate shock, 2025 tariff episode (Fernandez-Mejia et al., 2025).
  • Predictive power: Twice the cross-sectional return explanatory power of standard duration/spread models (PLOS ONE, 2015).
Feature Traditional Bond Strategies EntropiaTech Entropy Approach
Risk MeasureDuration / OAS / VaREntropy (full distributional complexity)
Allocation LogicIndex tracking / static tiltsEntropy-weighted, regime-adaptive
Credit Risk DetectionLagging — reacts to spread wideningLeading — entropy flags stress early
Tail RiskSystematically underweightedNaturally captured via distributional shape
Sector RotationDiscretionary / benchmark-relativeEntropy-triggered dynamic reallocation
Overlay TriggersNone / rules-based stop-lossPer-sector entropy score thresholds

Key Scientific References