HomeNewsEquity Valuation / Financial EngineeringPart 3: Defining Regimes via Fibonacci Splits & Scenario Forecasts

Part 3: Defining Regimes via Fibonacci Splits & Scenario Forecasts

Part 3: Defining Regimes via Fibonacci Splits & Scenario Forecasts

  • August 5, 2025
  • Posted by: Drglenbrown1
  • Category: Equity Valuation / Financial Engineering
No Comments
Part 3: “Defining Regimes via Fibonacci Splits & Scenario Forecasts”

Abstract: In Part 3, we translate the calibrated Expected Valuation Discount Factor (EVDF) into discrete market-regime scenarios—Bull, Base, and Bear—using Fibonacci splits. We then derive the corresponding Expected Valuation Growth Factors (EVGF) and generate explicit price forecasts for any horizon. Tesla (TSLA) serves as our running example, and we anchor each step in Dr. Glen Brown’s Nine Laws Framework.

1. Why Scenario Splits Matter

  • Capture Regime Dynamics: Markets oscillate between expansion (Bull), normalization (Base), and contraction (Bear).
  • Systematic Bands: Fibonacci ratios provide a time-tested method to partition ranges in nature and finance.
  • Transparency: Each regime’s EVDF & EVGF is explicit—no hidden adjustments.

2. Applying Fibonacci Splits to EVDF

Let EVDFbase be our recalibrated discount factor (from Part 2). Choose a split ratio r (e.g. 38.2 %). Then:

EVDFBull = EVDFbase × (1 – r)
EVDFBear = EVDFbase × (1 + r)
EVDFBase = EVDFbase

Reciprocal gives growth factors:

EVGFs = 1 / EVDFs
for s ∈ {Bull, Base, Bear}

3. Deriving Scenario Forecasts

For any horizon t (in years), the price forecast under scenario s is:

Forecastt,s = P₀ × (EVGF1yr,s)^t

where P₀ is today’s price.

4. Worked Example: Tesla (38.2 % Split, P₀ = \$308)

RegimeEVDFEVGF (1/EVDF)1-Yr Forecast5-Yr Forecast
Bull0.66161.5114308 × 1.5114 ≈ \$465.5308 × (1.5114)^5 ≈ \$2,429
Base1.07060.9341308 × 0.9341 ≈ \$287.7308 × (0.9341)^5 ≈ \$219
Bear1.47960.6759308 × 0.6759 ≈ \$208.2308 × (0.6759)^5 ≈ \$43

5. Nine-Laws Narratives

  • Law 2 – Weighted Decay of DAATS: Fibonacci splits mirror the memory decay kernel in your ATR-driven stop logic.
  • Law 1 – Correlation Regime Transition: Each regime band corresponds to a different correlation/noise environment.
  • Law 3 – Macro Shock Propagation: Sharp regime shifts can be modeled as discrete jumps between these bands.
  • Law 4–5: Stop recalibrations and event exits attach naturally to regime thresholds.

6. Practical Implementation

  1. Select Split Ratio (38.2 %, 50 %, 61.8 %) based on back-tested regime turnover.
  2. Compute EVDFs & EVGFs in your spreadsheet or script.
  3. Populate Forecast Table for chosen horizons (1, 3, 5 years).
  4. Feed Results into your probability-weighting engine (Part 4) and MOS layer.

About the Author

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

Business Model Clarification

All research and software models are proprietary and for internal use only.

Risk Disclaimer

Educational purposes only. Trading and investing carry risks; past performance is not indicative of future results.



Leave a Reply