EntropiaTech
Overview Performance Positioning Entropy Research
NAV 375.82  ·  August 31, 2026
EntropiaTech · Global Equities

Pure equity alpha
powered by entropy
regime intelligence

A dynamic global equity strategy harnessing entropy signals to time allocation across world equity markets — capturing upside momentum while detecting regime shifts early.

+275.82%
Total Return
1.76
Sharpe Ratio
-12.55%
Max Drawdown
NAV · BASE 100 · NOV 2018 – JUL 2026

Key Metrics

Strategy Overview

As of August 31, 2026  ·  Net of fees  ·  Base 100 at inception (November 06, 2018)

375.82
Latest NAV
Base 100
+275.82%
Total Return
Since inception
+17.77%
Ann. Return
Since inception
1.76
Sharpe Ratio
Since inception
-12.56%
Max Drawdown
February 18, 2025
10.08%
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  ·  65/94 positive months (69%)  ·  Green = positive  ·  Red = negative
Metric Since InceptionLast 12 Months
Total Net Return+275.82%+11.63%
Annualised Return+17.77%—
Annualised Volatility10.08%7.94%
Sharpe Ratio1.761.29
Calmar Ratio1.42—
% Positive Months69.15% (65/94)—
Latest NAV (Base 100)375.82—
Strategy InceptionNovember 06, 2018—
Report DateAugust 31, 2026—
Maximum Drawdown — UCITS Peak-to-Valley
MDD: -12.56%  ·  Peak: February 18, 2025  ·  Trough: April 07, 2025
Drawdown duration: 35 business days (48 cal. days)  ·  Recovery date: June 03, 2025
Time to recovery: 42 business days (57 cal. days)  ·  Full cycle: 77 business days (105 cal. days)
Key Ratios
Since inception vs. last 12 months

Allocation

Current Portfolio Exposure

As of August 31, 2026  ·  Notional weights by asset class  ·  Positive = long  ·  Negative = short / hedge

Asset Class Exposures
Entropy-driven equity allocation across global markets
Exposure Detail
Notional weights as of report date

Market Intelligence

Entropia Scores by Asset Class

As of August 31, 2026  ·  Score 0 = Maximum Calm  ·  Score 1 = Maximum Stress  ·  Portfolio average: 60.0%

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 global equity management

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 stress before it appears in second-moment statistics (Gradojevic & Caric, 2017).
  • Regime-blind: Vol cannot distinguish a calm trending market from a pre-crash accumulation 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 Entropy Approach
Risk MeasureBeta / standard deviationEntropy (full distributional complexity)
Allocation LogicMarket-cap weighted / factorEntropy-weighted, regime-adaptive
Regime DetectionLagging — reacts to realised volLeading — entropy flags stress early
Tail RiskSystematically underweightedNaturally captured via distributional shape
Drawdown ManagementPassive / discretionaryEntropy-triggered dynamic reduction
Overlay TriggersNone / ad hocPer-asset entropy score thresholds

Key Scientific References