Abstract: In this installment, we treat the Fundamental Value per Share (FV₀) and the observed market price (P₀) as dual aspects of a quantum system. By invoking the analogy of measurement and time evolution, we derive the Expected Valuation Discount Factor (EVDF) and its reciprocal, the Expected Valuation Growth Factor (EVGF), fully calibrated to “today’s” price. Tesla (TSLA) serves again as our concrete example, while quantum narratives illuminate each step.

1. Quantum Prelude: Value as a Wavefunction

In quantum mechanics, a system’s state is described by a wavefunction |ψ⟩, whose squared amplitude gives probabilities. Analogously, we view a stock’s Fundamental Value and Market Price as two “basis states” in a valuation Hilbert space:

• Basis |FV⟩: the unobserved, fundamentals-only state
• Basis |P⟩: the observed market-price state

Prior to measurement, the system exists in a superposition: \[ |\Psi⟩ = a\,|FV⟩ + b\,|P⟩ \] where amplitudes a and b encode our confidence in each state.

2. The Price Measurement Operator (Law 9)

Law 9 – Continuous Model Validation & Rebirth teaches that each time we “measure” price, we must renormalize our valuation state. Define the price operator ^P acting on |FV⟩:

^P |FV⟩ → P₀ |P⟩

Measurement collapses the superposition onto |P⟩ with eigenvalue P₀. To reconcile |FV⟩ and |P⟩, we solve for the discount factor that maps between them over an observed horizon tₒ𝒷ₛ months.

3. Time Evolution: From FV₀ to P₀

In QM, time evolution is governed by the unitary operator U(t)=e^−iĤt/ħ. Here we replace Ĥ with a “valuation Hamiltonian” whose action over t yields the EVGF:

EVGF₁yr = (P₀ / FV₀)^(12 / tₒ𝒷ₛ)
EVDF₁yr = 1 / EVGF₁yr

Just as U(t) evolves |ψ(0)⟩ → |ψ(t)⟩, EVGF “evolves” FV₀ into the observed price over a one-year equivalent horizon.

4. Worked Example: Tesla Calibration

For TSLA on August 4 2025:

• Fundamental Value: FV₀ = \$320.50
• Observed Price: P₀ = \$308
• Observation Period: tₒ𝒷ₛ = 7 months

EVGF₁yr = (308 / 320.50)^(12/7) ≈ 0.9341
EVDF₁yr = 1 / 0.9341 ≈ 1.0706

Interpretation: A one-year “valuation decay” factor of 1.0706 implies the market price reflects a ~6.6 % contraction from fundamentals when extrapolated to a full year.

5. Quantum Narratives & Nine-Laws Integration

• Law 1 – Correlation Regime Transition: Each recalibration is akin to a regime operator projecting onto new market conditions.
• Law 2 – Weighted Decay of DAATS: The “valuation Hamiltonian” incorporates ATR-based volatility memory, weighting our evolution operator.
• Law 3 – Macro Shock Propagation: Scattering events (macro shocks) broaden the EVDF’s variance, preparing for Monte Carlo in later parts.
• Law 4 – Exposure & Death-Stop: Even as we evolve value, we enforce a “death-stop” boundary—EVDF thresholds beyond which positions are reassessed.
• Law 5 – Exit Only on Death: Only a full collapse (price crash) triggers an exit, not minor fluctuations in EVDF.
• Law 6 – Adaptive Break-Even Decision: The break-even point aligns with the EVDF crossing back through unity.
• Law 7 – Portfolio-Level Noise Budget: This calibration feeds into the portfolio noise budget, allocating risk based on EVDF volatility.
• Law 8 – Transaction-Cost & Slippage Optimization: Automated recalibration minimizes costs of carrying stale valuations.
• Law 9 – Continuous Model Validation & Rebirth: The measurement ⇒ collapse ⇒ recalibration loop embodies continuous rebirth.

6. Practical Implementation

1. Automate the EVDF/EVGF solve in your spreadsheet or Python module, triggering on updated P₀ and FV₀.
2. Log each recalibration event as a “quantum measurement” with timestamp and regime flags.
3. Visualize EVDF drift over time as a probability amplitude decay curve.
4. Use this quantum-calibrated EVDF as the foundation for regime splits and probability weighting in Parts 3–4.

About the Author

Dr. Glen Brown is President & CEO of Global Accountancy Institute, Inc. and Global Financial Engineering, Inc., and architect of the Nine-Laws Framework and GATS system.

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