The GES Methodology

Step 1 — signal firing

Each signal is a standardized z-score. A signal "fires" when |z| ≥ 1.2, a threshold calibrated against eleven years of real signal history rather than assumed.

Step 2 — blend into an event probability

Five components, computed daily and blended by weight:

Each component is bucketed against its own empirical probability map, then combined by weight. Worked example, Geopolitical, 9 Oct 2016:

0.25(.25) + 0.25(.20) + 0.20(.90) + 0.15(.60) + 0.15(.80) = 0.5025

Each fired event is separately scored for severity — Low, Medium, High, or Critical, from the magnitude of the underlying move against eleven years of historical norms for that regime. Severity doesn't gate the status ladder below; it travels downstream into the evidence-tier framing and the client factsheet.

Step 3 — regime status, with memory

Probability alone doesn't set status — each regime has its own thresholds and its own breadth/persistence confirmation, and status has memory rather than resetting daily:

Worked example: today's reading alone would only clear "Emerging," but the regime stays Active because it escalated there on 20 Sep and hasn't faded since.

Step 1 — company-level score

Company GES = 100 × avg over 8 dimensions of (Exposure × Beta × Confidence × e⁻λ·age)

Each dimension comes from company fundamentals; Beta sets how sensitive that dimension is to the live regime, confidence reflects data quality, and decay ages out stale data (λ=0.0067/year — a year-old figure still carries ~99% weight):

Country risk isn't a separate ninth dimension — it's carried inside Revenue and Supply Chain, which is where geographic concentration actually shows up in a company's numbers. In the worked example, Welltower (Real Estate) scores 65.4 on consistently high exposure across every dimension; IBM scores 28.6 on roughly half the exposure with the same beta table — the gap is the business, not the model.

Step 2 — sector aggregation and relevance

Company scores roll up to a sector median first. Sector vulnerability is then a separate, empirical measurement layered on top: for every past instance of this regime, we look at how each sector's price actually moved relative to the broader market, and bucket that historical relative impact into ±1 (clearly vulnerable/beneficiary), ±0.5 (moderately so), or 0 (no consistent relationship) — refreshed quarterly as more history accumulates. This is what turns "Technology has a high company GES" into "Technology has historically sold off under this regime, so that GES should count against the portfolio, not for it." Here: Financials, Consumer Discretionary, Technology, and Energy score vulnerable; Real Estate, Utilities, Health Care, Consumer Staples, and Materials score beneficiary.

Step 3 — portfolio aggregation and direction

Portfolio GES = Σ (holding weight × company GES)

Vulnerable- and beneficiary-sector weight are summed directly; a 5% materiality floor on either side sets the direction label — Vulnerable, Beneficiary, Mixed, or Neutral. A single concentrated name can also trip it on its own: a top holding contributing ≥2.0 points to Portfolio GES counts as material even in a sector that's under 5% of the book, so a large, singularly exposed position can't hide inside an otherwise-diversified sector weight. Worked example: GES 43.10, vulnerable 36.1%, beneficiary 50.4% → Mixed.

Step 1 — GES band and base action

Portfolio GES is bucketed into five empirically-set bands (Very Low through Very High, set from this portfolio universe's real achievable range rather than an assumed 0–100 scale). Regime Status × GES Band looks up one of 35 pre-defined base actions — e.g. Active + Very High → Trim + Replace; Watch is always Monitor.

Step 2 — action intensity

Intensity = Base Intensity(status) × (Portfolio GES / 100) × Client mandate multiplier

Base intensity is fixed per status (Active = 12%, rising through Confirmed and Severe), then scaled by the portfolio's own GES.

Client mandate — the multiplier and the overlay

The same regime, portfolio, and base action produce four different outcomes depending on mandate:

All three overlay rules were added after backtest evidence: the earlier Low Turnover trim paid transaction cost for a 23% historical win rate; Rotation used to fire even at Very Low band (52% win rate there vs. 60–75% at every other band); and Rotation used to fire regardless of whether that regime/action combination had ever been seen enough times to trust — it's now blocked whenever the historical evidence tier for that pairing is Weak or Insufficient (see Track Record), so the mandate with the most latitude doesn't also get the least discipline.

Step 3 — multi-regime overlay

Regimes rarely run alone. Every live regime is ranked and votes sector-by-sector on direction alongside the others:

Worked example: 4 regimes are live; 7 of 9 sectors agree, 2 conflict at 0.62× → portfolio multiplier 0.9156, taking Balanced intensity from 5.17% to 4.74%.

Step 1 — trim candidate scoring

Trim Score = Position weight % × Company GES × Vulnerability mult. × Liquidity mult.

Every holding in a vulnerable sector is scored, not just the largest name; the adjustment budget is split pro-rata by score share — a diversified trim, not a single stock call.

Step 2 — replacement scoring

Replacement Score = .35×GES benefit + .20×Sector fit + .20×Factor sim. + .10×Liquidity + .10×Confidence − .05×Valuation

Candidates are drawn from outside current holdings, ranked and pooled across every trimmed name. Factor similarity and valuation penalty are currently fixed neutral proxies (50 and 0) — no factor model or valuation pipeline exists yet, so ranking today runs on GES benefit, sector fit, liquidity, and confidence.

Step 3 — execution style by mandate

The same sizing executes differently by style: replace (sell + buy) for Balanced/Defensive, rotate (sell vulnerable, buy existing beneficiary holdings) for Regime Rotation, or a stand-down to Monitor below conviction. A 3%-of-turnover trading cost is charged against every simulated action.