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Probabilistic Valuation: Scenario Analysis, Decision Trees and Simulations

This chapter explores three probabilistic valuation techniques—scenario analysis, decision trees, and Monte Carlo simulations—that model discrete and continuous risk outcomes rather than collapsing risk into a single discount rate. Each method provides different advantages: scenario analysis for key outcome variations, decision trees for sequential risks, and simulations for comprehensive multi-variable uncertainty. The article emphasizes that expected values from these techniques are not inherently risk-adjusted and must be paired with appropriate discount rates to avoid double-counting risk.

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Metrics in this report

Beta

1.36coefficient

3M based upon business portfolio composition

Cost of Capital

10%percent

Biotech pharmaceutical firm in FDA approval process (Illustration 3.2)

Equity Risk Premium

4%percent

historical average

S&P 500 implied premiums 1960-2007 (3M valuation example)

Phase 1 FDA Approval Success Rate

70%percent

Pharmaceutical drug development

Phase 2 FDA Approval Success Rate (Type 1 Diabetes)

30%percent

Pharmaceutical drug development

Phase 3 FDA Approval Success Rate

80%percent

Single disease indication (Type 1 or Type 2 diabetes only)

Phase 3 FDA Approval Success Rate

75%percent

Dual disease indication (Type 1 and Type 2 diabetes)

Reinvestment Rate

30%percent

3M historical baseline assumption

Return on Invested Capital

25%percent

median

3M manufacturing company assumed maintenance level

Simulation Value per Share (3M)

87.35dollars

average across 10,000 runs

Monte Carlo simulation of 3M valuation

Standard Deviation of Simulated Values (3M)

16.15dollars per share

Range of valuation outcomes across 10,000 simulation runs