By Dr. Glen Brown • August 12, 2025

Abstract. The foreign exchange market has long lacked a unified valuation anchor comparable to equity markets’ earnings and multiples. The Dr. Glen Brown Forex Valuation Model (DGB-FVM) introduces a rigorous, quantifiable fair-value framework that fuses macroeconomic drivers, market-equilibrium deviations, and risk-sentiment premia into a single normalized index, designed to operate across strategic and tactical horizons. Crucially, DGB-FVM is integrated with GATS to ensure that valuation alignment and technical structure co-confirm entries, exits, and portfolio tilts. This paper outlines the philosophy, data architecture, normalization and weighting, governance, and a worked EURUSD example.

1) Introduction — Why FX Needs a Valuation Anchor

Equities enjoy well-understood anchors—earnings power, cash flows, multiples—while currencies are often traded on relative narratives, interest-rate expectations, and momentum. Without a consistent valuation compass, positioning can drift with sentiment. DGB-FVM supplies that compass: a coherent fair-value signal constructed from measurable macro differentials, deviations from equilibrium (PPP/REER/IRP), and a compact risk-sentiment layer. The output is a normalized Fair-Value Delta (FVD) that can be directly consumed by GATS for regime gating, risk budgeting, and portfolio construction.

2) Philosophical Foundation — Relativity, Triangulation, Integration

• Relativity Principle: No currency has absolute value; value is always expressed versus another. Therefore, all inputs are differentials or deviations between two economies/markets.
• Triangulation of Value:
  1. Fundamental Value — growth, inflation, external balances, policy.
  2. Market Equilibrium — PPP, REER, yield spreads, IRP.
  3. Strategic/Risk Value — country risk premia, global risk appetite, trade-weighted strength.
• Integration: The model’s output is not a trade by itself; it is a filter and bias that synchronizes with GATS technical structure and DAATS-anchored risk management.

3) Model Architecture — Inputs & Data Map

3.1 Fundamental Differentials (pair A/B)

• ΔGDP (growth differential)
• ΔCPI, ΔPPI (inflation differential)
• ΔIR (policy-rate & curve differentials; front-end and 10Y)
• ΔFiscal (budget balance %GDP differential)
• ΔCA (current account %GDP differential)

3.2 Equilibrium Deviations

• PPPΔ (Purchasing Power Parity deviation from long-run mean)
• REERΔ (Real Effective Exchange Rate deviation from band/mean)
• IRPΔ (interest-rate parity: spot vs. forward-implied)
• YieldΔ (sovereign 10Y spread deviation from mean)

3.3 Risk & Sentiment Layer

• CDSΔ (sovereign risk differential)
• Global Risk Factor (risk-on/off proxy scaled to FX vol)
• TWΔ (trade-weighted currency strength deviation for each leg)

4) Normalization, Scaling, and Weights

All raw inputs are transformed into comparable, unit-free scores using a robust normalization pipeline:

1. Stationarity & Winsorization: Convert to changes/ratios where appropriate, winsorize outliers (e.g., 1st/99th pct) to stabilize tails.
2. Z-Score Normalization: z_n = (x_n − μ_n) / σ_n with rolling windows (e.g., 5–10 years) to capture regime evolution.
3. Weighting: Initial weights w_n from historical regression of future 6–12M FX returns on z-scores; constrained optimization enforces parsimonious weights and caps the aggregate impact (e.g., |Σw_n z_n| ≤ 0.15).

Core valuation form (display + plain text):

LaTeX: \[ FV_t = S_0 \times \left(1 + \sum_{n=1}^{N} w_n \cdot z_n \right) \]

Plain text: FV_t = S0 × (1 + Σ w_n·z_n)

The model reports a Fair-Value Delta (FVD) as a percent mispricing:
FVD_t = 100 × (FV_t / S_0 − 1).

5) Dual-Horizon Operation

• Strategic (Monthly): Full stack inputs; establishes directional bias and allocative tilts for 3–12 months.
• Tactical (Weekly): Faster components (IRPΔ, YieldΔ, Global Risk) update bias magnitude, not direction, unless regime break is detected.

6) GATS Integration — From Anchor to Execution

• Regime Filter: Only take longs when FVD > +X% and GATS higher-timeframe structure is bullish (EMA Zones aligned, HAS blue, GMACD up, ADX > threshold, etc.).
• Risk: DAATS/ATR-anchored stops as per your doctrine (e.g., use M60 Death-Stop for lower timeframes; exits only on Death or adaptive BE per Nine-Laws).
• Portfolio: Rank the 28 pairs by FVD; overweight undervalued legs, underweight overvalued, with correlation-aware risk budgets.

7) Model Governance — Recalibration & Robustness

• Rolling Refit: Re-estimate weights annually on a rolling 5-year window; verify stability with sub-periods.
• Stress Regimes: Validate performance through rate-shock windows, energy shocks, and risk-off cascades; cap factor contributions under stress.
• Drift Control: Lock total influence of all factors within a realistic band (e.g., ±10–15% fair-value swing in extreme conditions).

8) Worked Example — EURUSD (as of August 12, 2025)

Purpose: Demonstrate the practical pathway from data to a fair-value read. Numbers below are illustrative to show mechanics; your production pipeline will source official time-series, compute rolling μ/σ, and output calibrated FVD.

8.1 Input Snapshot (directional illustration)

• ΔGDP: Slight U.S. growth edge ⇒ negative for EURUSD (EUR weaker vs USD).
• ΔCPI: Eurozone inflation near target; relative inflation gap modest ⇒ small positive for EUR or neutral.
• ΔIR: Short-rate and curve still USD-tilted ⇒ negative EURUSD.
• PPPΔ / REERΔ: EUR below long-run PPP/REER midpoint ⇒ positive EURUSD (mean-reversion tailwind).
• YieldΔ: 10Y spread modestly USD-favoring ⇒ small negative EURUSD.
• CDSΔ / Global Risk: Benign systemic risk ⇒ mild support for carry-positive leg; net modest effect.
• TWΔ: Trade-weighted EUR mixed; net small positive or neutral.

8.2 Normalized Signals (illustrative z-scores)

Aggregate signal: Σ(w·z) = −0.084 (i.e., −8.4%).

Let spot be S₀ (example: 1.1610). Then:

LaTeX: \[ FV = S_0 \times (1 + \Sigma w_n z_n) = 1.1610 \times (1 – 0.084) \approx 1.062 \]

Plain text: FV ≈ 1.1610 × 0.916 = 1.062

Interpretation: The model’s illustrative calibration suggests EURUSD is overvalued by roughly +9.3% versus fair value (FVD ≈ −8.4% → FV below spot). In practice, we would publish the exact FVD with confidence bands and a governance note on factor stability.

8.3 Trade & Portfolio Use

• Bias: Bearish (valuation overhang).
• Execution: Only short when GATS higher-timeframe structure is bearish (EMA Zones aligned down; HAS red; GMACD down; ADX > threshold). If technicals are bullish, we wait—valuation is a filter, not a trigger.
• Risk: DAATS-anchored stops per your doctrine (e.g., M60 Death-Stop for lower TF entries; exit only on Death or adaptive BE).
• Portfolio: Rank all 28 pairs by FVD; allocate risk to the most mispriced where technicals agree, respecting correlation and liquidity.

9) Extensions — Reaction Functions & AI Assist

• Central-Bank Reaction: Add policy-rule residuals (Taylor-style gaps) as inputs when material.
• AI Layer: Regime detection and non-linear factor interactions; dynamic weight smoothing during stress.
• Live Ops: Monthly strategic release with weekly tactical updates; change-log for factor breaks.

10) Conclusion

DGB-FVM provides the FX market with a missing anchor: a disciplined, data-driven fair-value signal that is aware of macro reality, market equilibrium, and risk conditions—and that plugs directly into a professional execution engine (GATS). This is the foundation for a new benchmark: the closest analogue to a “P/E for FX.”

About the Author: Dr. Glen Brown

Dr. Glen Brown is President & CEO of Global Accountancy Institute, Inc. and Global Financial Engineering, Inc.—global multi-asset proprietary trading firms. With over a quarter-century in markets, he is a financial engineer and the architect of the Global Algorithmic Trading Software (GATS), the Dynamic Adaptive ATR Trailing Stops (DAATS), and the Nine-Laws Framework for Adaptive Volatility & Risk Management. Dr. Brown’s work integrates quantitative rigor with disciplined execution, spanning currencies, equities, indices, commodities, and digital assets. His leadership philosophy—combining intellectual mastery with principled risk—shapes a closed, research-driven trading culture focused on innovation, resilience, and long-term compounding.

Professional profile: View full biography.

Business Model Clarification

Global Accountancy Institute, Inc. and Global Financial Engineering, Inc. operate as closed proprietary trading firms. We do not solicit or accept external clients, outside capital, or offer investment advisory services to the public. All research, models, and software—including GATS, DAATS, and the DGB-FVM—are developed for internal use by our proprietary teams.

General Risk Disclaimer

Educational Purpose Only. The information herein is for research and educational purposes and does not constitute investment advice, an offer, solicitation, or recommendation to buy or sell any financial instrument.

Market Risk. Trading foreign exchange, futures, equities, and digital assets involves substantial risk and is not suitable for all investors. Leverage can work for or against you. You may sustain a loss of some or all of your capital. Past performance is not indicative of future results.

Model Risk. All models, including DGB-FVM and GATS components, rely on assumptions and data that can change without notice. Model outputs are conditional, may be incomplete, and can be wrong—especially during regime shifts, illiquidity, or macro shocks.

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