EntropiaTech
Overview Performance Positioning Entropy Research
NAV 370.51  ·  September 30, 2026
EntropiaTech · Global Equities Hedged

Equity upside with
entropy-driven
downside protection

A dynamic global equity strategy combining entropy-based regime detection with systematic hedging overlays — capturing equity growth while actively limiting drawdowns across all market cycles.

+267.26%
Total Return
2.01
Sharpe Ratio
-8.60%
Max Drawdown
NAV · BASE 100 · NOV 2018 – JUL 2026

Key Metrics

Strategy Overview

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

367.26
Latest NAV
Base 100
+267.26%
Total Return
Since inception
+17.23%
Ann. Return
Since inception
2.01
Sharpe Ratio
Since inception
-8.60%
Max Drawdown
February 18, 2025
8.58%
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  ·  66/95 positive months (69%)  ·  Green = positive  ·  Red = negative
Metric Since InceptionLast 12 Months
Total Net Return+267.26%+4.25%
Annualised Return+17.23%—
Annualised Volatility8.58%7.13%
Sharpe Ratio2.010.50
Calmar Ratio2.000.41
% Positive Months69.47% (66/95)—
Latest NAV (Base 100)367.26—
Strategy InceptionNovember 06, 2018—
Report DateSeptember 30, 2026—
Maximum Drawdown — UCITS Peak-to-Valley
MDD: -8.60%  ·  Peak: February 18, 2025  ·  Trough: April 07, 2025
Drawdown duration: 35 trading days (48 calendar days)  ·  Recovery date: May 13, 2025
Time to recovery: 27 trading days (36 calendar days)  ·  Full cycle: 62 trading days (84 calendar days)
Key Ratios
Since inception vs. last 12 months

Allocation

Current Portfolio Exposure

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

Asset Class Exposures
Entropy-weighted equity allocation + active hedging overlays
Exposure Detail
Notional weights as of report date

Market Intelligence

Entropia Scores by Asset Class

As of September 30, 2026  ·  Score 0 = Maximum Calm  ·  Score 1 = Maximum Stress  ·  Portfolio average: 59.0%

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 modern equity portfolios

The Limits of Volatility

Standard deviation assumes Gaussian returns and symmetric risk — both systematically violated in equity markets.

  • Non-normality: Equity returns exhibit excess kurtosis and negative skewness (Mandelbrot, 1963; Fama, 1965) — variance is blind to this.
  • Asymmetry blindness: Variance penalises upside and downside equally — inconsistent with investor preferences (Kahneman & Tversky, 1979).
  • Backward-looking: Realised volatility rises after a crash. Entropy detects distributional stress before it materialises in second-moment statistics (Gradojevic & Caric, 2017).
  • Regime-blind: Vol cannot distinguish a calm trending market from a pre-crash accumulation phase with similar vol but very different tail risk.

Shannon Entropy as Risk Measure

First formalised by Claude Shannon (1948), entropy captures the full informational complexity of any 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 equity crash 1998–2026: Dot-com, GFC, COVID, 2025 tariff shock (Fernandez-Mejia et al., 2025).
  • Predictive power: Twice the cross-sectional return explanatory power of CAPM beta (PLOS ONE, 2015).
Feature Traditional Equity Strategies EntropiaTech Hedged Approach
Risk MeasureBeta / standard deviationEntropy (full distributional complexity)
Drawdown ManagementPassive — static stop-loss or noneDynamic entropy-triggered overlays
Regime DetectionLagging — reacts to realised volLeading — entropy flags stress early
Tail RiskSystematically underweightedHedged via VIX & CDS overlays
Equity ParticipationFull beta, unconstrainedEntropy-weighted, dynamically adjusted
Overlay TriggersNone / discretionaryPer-asset entropy score thresholds

Key Scientific References