Part 4: Probability Weighting & Margin-of-Safety
- August 5, 2025
- Posted by: Drglenbrown1
- Category: Equity Valuation / Financial Engineering
Abstract: In Part 4, we assign probabilities to each market-regime scenario—Bull, Base, Bear—then compute a single, probability-weighted expected value. We then layer a Margin-of-Safety (MOS) to produce a conservatively adjusted price target. Tesla (TSLA) illustrates each step, with narratives from Dr. Glen Brown’s Nine Laws Framework.
1. Why Probability Weighting?
- Blend Views: Rather than pick one regime, probability weights capture uncertainty across scenarios.
- Risk Management: Higher Bear probability limits overexposure to optimistic forecasts.
- Transparency: A clear formula ties each target to its odds.
2. Formula: Expected Forecast
E[FV₁] = p<sub>Bull</sub>·FV<sub>Bull</sub> + p<sub>Base</sub>·FV<sub>Base</sub> + p<sub>Bear</sub>·FV<sub>Bear</sub>
where FVs are the 1-year forecasts from Part 3 and ps are regime probabilities summing to 1.
3. Worked Example: Tesla 1-Year
| Regime | 1-yr Forecast (FV₁) | Probability (p) | Contribution |
|---|---|---|---|
| Bull | \$465.5 | 30 % | 0.30 × 465.5 = \$139.7 |
| Base | \$287.7 | 50 % | 0.50 × 287.7 = \$143.9 |
| Bear | \$208.2 | 20 % | 0.20 × 208.2 = \$41.6 |
| E[FV₁] (Unadjusted) | \$325.2 | ||
4. Margin-of-Safety (MOS)
Apply a conservative discount M (e.g. 15 %) to guard against forecast error:
E[FV₁]<sub>MOS</sub> = E[FV₁] × (1 – M) = 325.2 × 0.85 ≈ <strong>$276.4</strong>
This becomes our risk-adjusted 1-year target for Tesla.
5. Nine-Laws Narratives
- Law 1 – Correlation Regime Transition: Probabilities ps derive from regime signals (ATR, MACD, macro triggers).
- Law 7 – Portfolio-Level Noise Budget: Scenario odds allocate “noise budget” across regimes, optimizing risk share.
- Law 6 – Adaptive Break-Even Decision: MOS acts like a dynamic breakeven buffer, adjusting for uncertainty.
- Law 9 – Continuous Model Validation & Rebirth: Update probabilities and MOS as new data arrive to keep targets fresh.
6. Practical Implementation
- Compute Bull/Base/Bear forecasts from Part 3.
- Assign probabilities
pBull/Base/Bearvia your regime-prob model. - Calculate
E[FV₁]using the weighted-sum formula. - Apply MOS (10–20 %) to derive the final target.
- Automate in Excel/Python so updating P₀ or ps auto-recomputes the target.
About the Author
Dr. Glen Brown is President & CEO of Global Accountancy Institute, Inc. and Global Financial Engineering, Inc., and creator of the GATS and Nine-Laws Framework.
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